Genetic Testing in Oncology: Specific Tests

This LCD supplements but does not replace, modify or supersede existing Medicare applicable National Coverage Determinations (NCDs) or payment policy rules and regulations for genetic testing in oncology for these 10 specific tests. Federal statute and subsequent Medicare regulations regarding provision and payment for medical services are lengthy. They are not repeated in this LCD. Neither Medicare payment policy rules nor this LCD replace, modify or supersede applicable state statutes regarding medical practice or other health practice professions acts, definitions and/or scopes of practice. All providers who report services for Medicare payment must fully understand and follow all existing laws, regulations and rules for Medicare payment for genetic testing in oncology for these 10 specific tests and must properly submit only valid claims for them. Please review and understand them and apply the medical necessity provisions in the policy within the context of the manual rules. Relevant CMS manual instructions and policies may be found in the following Internet-Only Manuals (IOMs) published on the CMS Web site:

IOM Citations:

• CMS IOM Publication 100-02, Medicare Benefit Policy Manual,
• Chapter 15, Section 80.1 Clinical Laboratory Services and Section 280 Preventive and Screening Services
• CMS IOM Publication 100-08, Medicare Program Integrity Manual,
• Chapter 13, Section 13.5.4 Reasonable and Necessary Provisions in an LCD

Medicare National Correct Coding Initiative (NCCI) Policy Manual:

• Chapter 10, Section A Introduction

Social Security Act (Title XVIII) Standard References:

• Title XVIII of the Social Security Act, Section 1862(a)(1)(A) states that no Medicare payment may be made for items or services which are not reasonable and necessary for the diagnosis or treatment of illness or injury.

Code of Federal Regulations (CFR) References:

• CFR, Title 42, Volume 2, Chapter IV, Part 410.32(a), Part 410.32(d), and Part 410.32(e) Diagnostic x-ray tests, diagnostic laboratory tests, and other diagnostic tests: Conditions
• CFR, Title 42, Volume 2, Chapter IV, Part 411.15(k)(1) Particular services excluded from coverage
• CFR, Title 42, Volume 2, Chapter IV, Part 493 Laboratory Requirements

Compliance with the provisions in this LCD may be monitored and addressed through post payment data analysis and subsequent medical review audits.

History/Background and/or General Information

With advancement in science and technology comes the ability to incorporate genetic testing for oncology biomarkers into clinical care, with the goal of improved patient outcomes. The scope of this LCD encompasses the MAC’s review of 10 tests that are utilized in the practice of oncology in the Medicare population.

As defined by the Food and Drug Administration (FDA) and National Institutes of Health (NIH) Biomarker Working Group, a biomarker is “A defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or biological responses to an exposure or intervention, including therapeutic interventions,” and may include molecular, histologic, radiographic, or physiologic characteristics.^{1 (p.46)}

Cancer is a disease caused by changes or alterations to a person’s genome. Some genetic changes or alterations can be inherited (also known as germline mutations). About 5-10% of all cancer diagnoses result from germline mutations, and over 50 hereditary cancer syndromes are known. Other cancer-causing (oncogenic) genetic changes or alterations result from acquired genetic damage (also known as somatic mutations). Somatic mutations can arise in numerous scenarios, including exposure to chemicals that alter DNA (carcinogens) or ultraviolet (UV) radiation from the sun.²

Biomarker testing is a part of precision medicine (also known as personalized laboratory medicine). Precision medicine is a tailored approach to medical care and treatment. Because each patient has a unique combination of genetic heritage and somatic changes, and therefore, a unique pattern of biomarkers, precision medicine for oncology involves the use of biomarker testing to pinpoint the disease management needs of individual patients and avoid the use of treatments which are unlikely to be successful.³ Much of this testing involves direct evaluation of the genetics of the malignancy through various testing methodologies. These methodologies can include high level genetic evaluations such as karyotyping (analysis of chromosomes) to more detailed evaluations such as identifying specific pathogenic point variations (analysis of specific nucleotide changes).

Additionally, testing may be used to check for a single biomarker or multiple biomarkers at the same time via a multigene test or panel.² As a result, the growing compendium of products described as biomarkers requires careful evaluation to determine what testing configurations are reasonable and necessary under Medicare.

Biomarkers for oncology can be generally classified into 4 functional types⁴:

1. Diagnostic biomarkers detect or confirm the presence of a disease or condition.
2. Prognostic biomarkers provide information about the likely course of a disease process and potential patient outcomes if left untreated.
3. Predictive biomarkers forecast a patient’s response and/or benefit to a specific treatment.
4. Therapeutic biomarkers identify potential targets for a medical intervention. (e.g., targeted drug therapy)

In certain circumstances, genetic testing for oncology biomarkers in patients with the corresponding appropriate medical condition could have the potential to assist patient management in the Medicare population. However, given the complexity and rapidly expanding knowledge in this topic area, we must vigilantly avoid testing that generates confusion and that does not improve patient outcomes. In order for services to be considered reasonable and necessary, they must impact the management of the patient and lead to improved patient outcomes. Specialized clinical expertise in oncology in addition to advanced knowledge in both genetic variation and effect on gene function is required to facilitate optimal outcomes for patients.

Definitions

Analytical validation is a process intended to determine if a test, tool, or instrument has acceptable technical performance (sensitivity, specificity, accuracy, precision, etc.). Analytical validation is an assessment of the test’s technical performance (the test measures what it was designed to measure), not its usefulness or clinical significance.^1,5 Analytical validity includes the ability of the test to accurately and reliably detect the mutation and/or variant.⁵

Biomarkers A biological molecule found in blood, other body fluids, or tissues that is a sign of a normal or abnormal process, or of a condition or disease. A biomarker may be used to see how well the body responds to a treatment for a disease or condition. Also called molecular marker and signature molecule.⁶

Cancer Screening An attempt to detect cancer early by routine examination of apparently healthy people.⁷

Cancer Surveillance is closely watching a patient’s condition but not treating it unless there are changes in test results. Surveillance is also used to find early signs that a disease has come back. During surveillance, certain exams and tests are done on a regular schedule.⁸

Cell-free DNA (cfDNA) is a laboratory method that involves analyzing free (i.e., no longer within the cell) DNA contained within a biological sample, most often to look for genomic variants associated with a hereditary or genetic disorder. For example, prenatal cfDNA testing is a non-invasive method used during pregnancy that examines the fetal DNA that is naturally present in the maternal bloodstream. Cell-free DNA testing is also used for the detection and characterization of some cancers and to monitor cancer therapy.⁹

Clinical Indication for Germline testing is a sign, symptom, laboratory test result, or medical condition, or a combination of these indications, that leads to the recommendation of a treatment, laboratory test, or procedure for a hereditary disease or condition.¹⁰

Circulating tumor DNA (ctDNA) are small pieces of DNA that are released into a person’s blood by tumor cells as they die. A sample of blood can be used to look for and measure the amount of ctDNA and identify specific mutations (changes) in the DNA. Circulating tumor DNA is being used as a biomarker to help diagnose some types of cancer, to help plan treatment, or to find out how well treatment is working or if cancer has come back.¹¹

Clinical validity is defined as the ability of a test to classify a patient’s specific circumstance into a diagnostic, prognostic, or predictive functional category. It should be noted that clinical validity is not a fixed value. Clinical validity includes the ability of the test to accurately and reliably detect the disease of interest in the defined population. ^5,12

Clinical utility is defined as the ability of a test to provide information related to the patient’s care and management, and thus, its ability to inform treatment decisions. CMS is most focused on assessing clinical utility in the context of whether or not a test is used to guide patient management and whether or not use of the test results leads to treatment that improves health outcomes.^5,12

Comprehensive Genomic Profiling (CGP) is, at this time, a term with many potential interpretations depending on which entity uses the term and in what context the term is used. Because of this, the term Comprehensive Genomic Profiling will not be utilized within this LCD. It is recognized that knowledge bases like NCCN sometimes use the term CGP, but it must be recognized that the term’s precise definition depends on the unique context in which it is utilized. Because of this, the way CGP is defined in one guideline will not necessarily translate to other guidelines.

FDA-cleared or approved test system means a test system was cleared or approved by the FDA through the premarket notification (510(k)) or premarket approval (PMA) process for in-vitro diagnostic use. (See CFR, Title 42, Volume 2, Chapter IV, Part 493.2 Laboratory Requirements: Definitions)

Genetic Testing, for the purposes of this LCD, describes any and all assays evaluating DNA and/or RNA without regard for a test’s purpose, methodology, or output. Examples of “genetic testing” include DNA sequencing of genes and surrounding non-coding regions, quantification of RNA expression, identification of epigenetic changes to the DNA, and evaluation of overall changes in chromosome structure. Moreover, this term encompasses all testing that includes genetic evaluation without regard to other included test components such as simultaneous protein testing and algorithmic analyses (e.g., multianalyte assays with algorithmic analyses [MAAAs]). Widely accepted terminology used in oncology such as “biomarker testing,” “genetic testing for an inherited mutation,” and “genetic testing for inherited cancer risk” are included under the umbrella term “genetic testing” for the purposes of this LCD, recognizing that molecular oncology is a highly complex and rapidly evolving field and thus, more inclusive terminology is required.

Genomic Testing, for the purposes of this LCD, will not be used as a defining term. Depending on the context, genomic testing can describe testing that includes both genes and non-coding sequence; however, given how this term is not always used precisely, we chose to avoid using this term to define policy in this LCD.

Genomic Sequencing Procedures (GSPs) are DNA and/or RNA sequence analysis methods that simultaneously assay multiple genes or genetic regions relevant to a clinical situation. They may target specific combinations of genes or genetic material or assay the exome or genome.¹³

Germline The sequence of cells in the line of direct descent from zygote to gametes, as opposed to somatic cells (all other body cells). Mutations in germline cells are transmitted to offspring; mutations in somatic cells are not transmitted to offspring.¹⁴

Kit means all components of a test that are packaged together. (See CFR, Title 42, Volume 2, Chapter IV, Part 493.2 Laboratory Requirements: Definitions)

Laboratory Developed Tests (LDT) defined by the FDA as an in vitro diagnostic test that is manufactured by and used within a single laboratory.¹⁵

Liquid biopsy is a test performed on blood to either look for cancer cells circulating in the blood or for DNA from tumor cells that are in the blood.¹⁶

Minimal residual disease (MRD) is a term used to describe a very small number of cancer cells that remain in the body during or after treatment. Minimal residual disease can be found only by highly sensitive laboratory methods. Also called measurable residual disease.¹⁷

Multianalyte Assays with Algorithmic Analyses (MAAAs) are procedures that utilize multiple results derived from panels of analyses of various types, including molecular pathology assays, fluorescent in situ hybridization assays, and non-nucleic acid-based assays. Algorithmic analysis using the results of the assays as well as other patient information is performed and typically reported as a numeric score(s) or as a probability.¹⁸

Neoplasm An abnormal mass of tissue that forms when cells grow and divide more than they should or do not die when they should. Neoplasms may be benign (not cancer) or malignant (cancer).¹⁹

Next Generation Sequencing (NGS) is a high-throughput method used to sequence a part or the whole of an individual’s genome. This technique utilizes DNA sequencing technologies that are capable of processing multiple DNA sequences in parallel. Also called massively parallel sequencing. Note that NGS is also utilized to analyze RNA, however, the RNA is typically converted to complementary DNA (cDNA) before analysis.²⁰

Reflex testing means confirmatory or additional laboratory testing that is automatically requested by a laboratory under its standard operating procedures for patient specimens when the laboratory's findings indicate test results that are abnormal, are outside a predetermined range, or meet other pre-established criteria for additional testing. (See CFR, Title 42, Volume 2, Chapter IV, Part 493.2 Laboratory Requirements: Definitions)

Risk Factor for Germline testing is a variable associated with an increased risk of a disease, such as age, gender, or family history of disease.¹⁰

Somatic, a term synonymous with acquired, refers to genetic code alterations that develop after birth (e.g., occurring in neoplastic cells)²¹

A Substantiated Suspicion of Cancer requires direct, physical sampling of a lesion, such as a needle aspiration or excision of tissue, followed by microscopic (histologic or cytologic) or flow cytometric evaluation of the sample. For the purposes of this LCD, radiologic suspicion of cancer is not considered “substantiated.” (Except where otherwise specified in the Covered Indications as reasonable and necessary.)

Treating physician is the physician who is treating the beneficiary, that is, the physician who furnishes a consultation or treats a beneficiary for a specific medical problem and who then uses the results in the management of the beneficiary's specific medical problem. Tests not ordered by the physician who is treating the beneficiary are not reasonable and necessary. Nonphysician practitioners that are enrolled in the program, who are operating within the scope of their authority under State law and within the scope of their Medicare statutory benefit, are also considered for this purpose, as treating and managing a beneficiary’s specific medical problem. (see CFR, Title 42, Volume 2, Chapter IV, Part 410.32(a))

Tumor mutation burden (TMB) is the total number of mutations (changes) found in the DNA of cancer cells.²²

Covered Indications

For services to be considered reasonable and necessary, the below is required:

The provider has either established a diagnosis of cancer or found significant evidence to create suspicion for cancer in their patient. (See SSA 1862(a)(1)(A)) Both a clinical evaluation AND abnormal results from histologic, cytologic and/or flow cytometric examination are required to establish a diagnosis of cancer or suspicion of cancer. If then, as a next step in the clinical management of the patient, genetic testing would directly impact the management of the patient’s specific condition, the testing would be indicated. (see CFR, Title 42, Volume 2, Chapter IV, Part 410.32(a))

UroVysion has been determined to demonstrate actionability in clinical decision-making as described below:

1. UroVysion: This FDA-approved adjunctive test is intended for use in conjunction with and not in lieu of current standard diagnostic procedures to aid in the initial diagnosis of bladder carcinoma in patients with persistent hematuria and subsequent monitoring for tumor recurrence in patients previously diagnosed with bladder cancer following administration of Bacillus Calmette-Guerin (BCG).²³ Therefore, UroVysion is reasonable and necessary when at least one of the following conditions is met:
   1. To clarify indeterminate or nondiagnostic cytology results in patients with persistent gross or microscopic hematuria suspected of having urothelial cancer following a complete history and physical, cystoscopy, and imaging of the urinary tract in alignment with published, peer-reviewed specialty society guidelines.^24,25,26 OR
   2. Surveillance in patients already diagnosed with non-muscle invasive bladder cancer (NIMBC) under evaluation for the response to intravesical BCG in alignment with published, peer-reviewed specialty society guidelines²⁶

Limitations

The following are considered not reasonable and necessary:

1. Genetic testing in patients who do not have either an established diagnosis of cancer or substantiated suspicion of cancer as determined by a clinical evaluation and abnormal results (cancer or suspicious for cancer) from histologic, cytologic, and/or flow cytometric examination. (Except where otherwise specified in the Covered Indications as reasonable and necessary) (See SSA Section 1862(a)(1)(A))
2. Genetic testing of asymptomatic patients for the purposes of screening the patient or their relatives. (See SSA Section 1862(a)(1)(A))
3. Repetitions of the same genetic test on the same genetic material. (i.e., revalidations, quality control, or failure of a first test attempt)(see Medicare NCCI Policy Manual, Chapter 10, Section A Introduction)
4. DecisionDx-SCC*
5. Cxbladder Detect*
6. Enhanced Cxbladder Detect*
7. Cxbladder Monitor*
8. Cxbladder Triage*
9. Colvera*
10. PancreaSeq Genomic Classifier*
11. PancraGEN*
12. ThyroSeq CRC*

*Please see below Summary and Analysis of Evidence sections for citations.

Provider Qualifications

The following provider qualification requirements must be met for the service to be considered reasonable and necessary. The ordering provider of a genetic test for a patient with an established diagnosis of cancer or substantiated suspicion of cancer must:

• Be the treating clinician who is responsible for the management of the patient’s cancer; and,
• Understand how the test result will impact the patient’s condition; and,
• Have presented this information to the patient eliciting patient understanding.

(See Code of Federal Regulations, Title 42, Volume 2, Chapter IV, Part 410.32(a)).

Notice: Services performed for any given diagnosis must meet all of the indications and limitations stated in this LCD, the general requirements for medical necessity as stated in CMS payment policy manuals, any and all existing CMS national coverage determinations, and all Medicare payment rules.

Introduction

Please refer to the “History/Background and/or General Information” section for general information on testing of RNA and DNA as it applies to oncology.

This evidence review focuses on genetic testing used to guide oncologic treatment and whether the evidence behind this testing is adequate to draw conclusions about improved health outcomes for the Medicare population. In general, health outcomes of interest include patient mortality, morbidity, quality of life, and function.

For use in the Medicare population, tests themselves must demonstrate analytic validity, clinical validity, and clinical utility. Tests should enhance clinical decision-making, directly informing clinical management and improving patient outcomes.

In the context of oncology, genetic testing endeavors to improve patient outcomes through both prognostic and predictive means. For instance, oncologic genetic testing can optimize treatment choice (predictive), avoiding ineffective treatments and reducing adverse events. Ultimately, patient-centered outcomes must be the underlying justification for oncologic testing.

Summary of Evidence for Specific Lab Tests

Cxbladder (Detect, Triage, Triage Enhanced, Monitor)

In addition to the articles submitted with comments, PubMed and Google Scholar were searched for peer-reviewed, evidence-based literature which provided information regarding analytic and clinical validity and clinical utility for the Cxbladder test. Key words used to search in combination included: Cxbladder, Cxbladder detect, Cxbladder triage, Cxbladder monitor, molecular testing, urine test, bladder cancer, urine biomarker(s), mRNA, uRNA, gene expression profile test, GEP test, 5 gene expression assay, prognostic, and TERT and FGFR3 mutations.

Before summarizing the literature underlying the development and validation of Cxbladder tests, overarching concepts and definitions will be discussed. Cxbladder tests are prognostic in that they do not diagnose cancer but rather provide the likelihood that cancer is present in the patient. Cxbladder tests all share the same molecular analysis of 5 genes: CDC2, MDK, IGFBP5, HOXA13 and CXCR2. This analysis, known as a Gene Expression Profile (GEP), looks at how much transcriptional activity (production of mRNA) is seen per gene and uses this data to predict the likelihood of cancer. Since the transcriptional activity of a gene is a continuous variable (e.g., any number between 1 and 10, as opposed to either 1 or 10), determining where the cutoff, or threshold, between a positive result and a negative result can be changed depending on how a test is used. This means that if a test is used to identify all cancer signals, no matter how weak, the cutoff would be moved to ensure no signal is missed at the expense of also giving more falsely positive results (higher sensitivity). On the other hand, if the test is designed to provide high confidence that a positive result is truly positive, the cutoff would be moved in the opposite direction at the expense of providing more falsely negative results (higher specificity). Since cutoffs can be adjusted, different versions of the Cxbladder test (using the same 5 genes) are used to provide slightly different answers depending on which statistical analyses are most important. Additionally, different versions of Cxbladder tests are designed for different clinical circumstances. Some tests assist in determining the level of concern for cancer in patients with hematuria while other tests help determine the risk of cancer recurrence. These tests are considered helpful in reducing unnecessary procedures, such as cystoscopy, when the results indicate a low likelihood for the presence of cancer.

The Cxbladder line of tests are currently represented by 6 versions of a 5 gene expression profile: Cxbladder Detect, Cxbladder Triage, Cxbladder Monitor, Cxbladder Resolve, enhanced Cxbladder Triage, and enhanced Cxbladder Detect. The sequential development of each test version may be traced through a series of publications, beginning in 2008 with a paper describing the development of Cxbladder’s precursor, the uRNA test, a 4 gene expression profile (GEP) test.⁴⁰ Each of the Cxbladder tests rely on a different set of statistical parameters to optimize the function of the 5 gene expression assay, sometimes synthesizing gene expression data with other inputted data such as patient demographics, cancer history, other clinical history, and single nucleotide variants associated with the FGFR3 and TERT genes. Cxbladder Detect is optimized for sensitivity and specificity as initially described in the seminal 2012 paper.²⁹ Both Cxbladder Triage and Cxbladder Monitor are optimized for sensitivity, Negative Predictive Value (NPV), and test negative rate as initially described in 2015 and 2017 papers, respectively.^30,31 Cxbladder Resolve, with a published validation in 2021, is optimized for sensitivity and specificity.³² Most recently, in 2022, the enhanced versions of Cxbladder Triage and Detect were described, with the basic purpose of each parent test unchanged but the parameters of each new test modified by adding data from sequencing 6 “single nucleotide polymorphisms” associated with 2 genes: FGFR3 and TERT.³³

In 2008, Holyoake and colleagues described a predicate test to the Cxbladder tests, uRNA-D.⁴⁰ The 4 gene (CDC2, MDK, IGFBP5, and HOXA13) expression profile test was designed to detect and characterize transitional cell carcinoma (TCC) from patients’ urine. During initial development of the test, over 26,000 RNA expression markers were compared between tissue from TCC and benign urothelium, and the 4 RNA expression markers that best detected and subtyped TCC were selected. Validation of this 4-marker test utilized urine samples from a cohort of 142 patients that was comprised of 75 patients who were diagnosed with Ta-T4 TCC tumors and 77 patients without TCC (however all these patients still had other diseases affecting their urologic tract). The overall specificity of this test was 85% with a range of sensitivities depending on tumor stage (from 48% for Ta tumors to 100% for tumors with a stage greater than T1).

In 2012, O’Sullivan and colleagues developed and validated the first Cxbladder test (Cxbladder Detect), using a foundation of the uRNA 4GEP test and adding an additional gene (CXCR2) to this profile that was “predicted to reduce the risk of false-positive results in patients with acutely or chronically inflamed urothelium.”²⁹ The 2012 publication also compared the new Cxbladder test to its precursor test (uRNA-D), urine cytology, and other urine tests on the market (NMP22 ELISA and BladderChek). The patient cohort was comprised of 485 patients presenting with gross hematuria. Cxbladder demonstrated an 81.8% sensitivity at a fixed specificity of 85%; all other tests in the comparison fell below a sensitivity of 65%, although the specificity of all other tests was higher than Cxbladder, with the highest specificity at 96.4% for the BladderChek test.

In 2015, Breen and colleagues further evaluated the Cxbladder Detect test in a comparative study with other tests used to detect urothelial carcinoma in urine.³⁴ The other tests evaluated included cytology, UroVysion FISH, and NMP22 BladderChek. The study utilized 5 cohorts of patients, only 1 of which evaluated all 4 tests for the entire cohort. Data from the 5 cohorts were evaluated and integrated, with several different imputation analyses utilized to fill in for missing test values and create a “new, imputed, comprehensive dataset.” From this data, the authors found that before imputation, Cxbladder Detect had superior sensitivity (79.5%) as compared to the other 3 tests (the second highest sensitivity being 45.5%) but inferior specificity (82.2%), with the second lowest specificity being 87.3%. Utilizing several different imputation methodologies, similar findings for comparative sensitivities and specificities were seen, leading to the conclusion that the imputed data sets were valid, with the best imputation methodology being the 3NN model. Finally, with the new imputed data set, the authors re-assessed the comparisons between tests and found that Cxbladder Detect “outperformed” the other tests in this study.

In 2015, Kavalieris and colleagues developed another version of the Cxbladder test (later to be called Cxbladder Triage), this time evaluating the impact of adding clinical data (age, gender, frequency of macrohematuria, and smoking history) to the testing algorithm.³⁰ Genetic input into the algorithm was termed the G INDEX while clinical data was termed the P INDEX. The study utilized 517 patients with macrohematuria from the 2012 Cxbladder study population, an additional 178 patients with macrohematuria from 2 separate cohorts, and 45 patients from a small cohort of patients with microhematuria.²⁷ Combining the G and P indices provided a better bias-corrected receiver operating characteristic curve (AUC) (0.86) than either of the indices alone (0.83 and 0.61 respectively). When set at a test-negative rate of 0.4, the G + P INDEX performed with a sensitivity of 95% and NPV of 98%, improving on the G INDEX sensitivity and NPV of 86% and 96% respectively. The authors envisioned the G + P INDEX being used to triage outpatients with a low probability of having urothelial carcinoma, reducing the need for diagnostic procedures.

In 2017, Kavalieris and colleagues developed another version of the Cxbladder test (Cxbladder Monitor) utilizing a cohort of 763 patients under surveillance for recurrence of bladder urothelial carcinoma.³¹ In addition to the data from the 5 gene expression profile, Cxbladder Monitor also used clinical data in its algorithm which included previous tumor status (primary tumor or recurrent tumor) and the number of years elapsed since the previous tumor. The paper analyzed several subgroups including different stages of tumor and patients who had received adjuvant bacillus Calmette-Guerin (BCG) treatment. With a test negativity rate of 0.34, Cxbladder Monitor demonstrated a sensitivity of 93% and NPV of 97%.

Also from 2017, Lotan and colleagues utilized the same patient cohort found in the Kavalieris (2017) study to perform a comparative analysis between Cxbladder Monitor and other noninvasive urine tests that were used to rule out recurrent urothelial carcinoma.^31,35 The authors found that Cxbladder “outperformed” all comparative tests (which included cytology, NMP22 ELISA, NMP22 BladderChek, and UroVysion FISH), with higher sensitivity (91% versus sensitivities ranging from 11% to 33%) and higher NPV (96% versus NPVs ranging from 86% to 92%).

In 2017, Darling and colleagues performed a clinical utility study for Cxbladder Triage and Detect.³⁶ The study used previously obtained clinical data and Cxbladder test results to create clinical scenarios for 12 urologists. These scenarios centered on patients presenting with hematuria and evaluated how the urologists would hypothetically evaluate these patients in the context of Cxbladder results. The study found that when the urologists had access to Cxbladder results, they would hypothetically change their clinical decisions in caring for these patients, ultimately leading to fewer invasive diagnostic procedures.

In 2018, Lough and colleagues performed a clinical utility study for Cxbladder Monitor.³⁷ The study used previously obtained clinical data and Cxbladder test results to create clinical scenarios for 18 physicians. These scenarios centered on patients with a history of urothelial carcinoma and evaluated how the physicians would hypothetically manage these patients in the context of Cxbladder results. The study found that when the physicians had access to Cxbladder results, they would hypothetically change their clinical decisions in caring for these patients, leading ultimately to fewer tests and procedures for patients classified as low risk by Cxbladder and an increased number of tests and procedures for patients classified as higher risk.

In 2019, Konety and colleagues performed a retrospective analysis of pooled data (from 4 patient cohorts) in the context of Cxbladder Triage, Detect, and Monitor.³⁸ A total of 436 samples were evaluated from patients with hematuria and 416 samples from patients with potential recurrence of urothelial carcinoma. These Cxbladder results were then compared with cytology results for the same samples. The authors found that overall, Cxbladder demonstrated a better NPV than cytology (97.4% versus 92.6%) and missed less tumors (false negatives) than cytology (8 missed versus 59 missed).

In 2020, Koya and colleagues performed a retrospective audit of a new surveillance protocol that incorporated Cxbladder Monitor for patients with a history of urothelial carcinoma.³⁹ The patients involved were divided into 2 cohorts: low risk (n = 161) and high risk (n = 47), noting that these numbers represent only patients who completed the study with both Cxbladder testing and follow up cystoscopy. There were 309 patients who were initially enrolled in the study but only 208 that completed the study. In the low-risk cohort, patients who received a negative result for the Cxbladder test were permitted to wait longer (12 months as opposed to within 2 to 3 months of a positive result) before receiving a follow-up cystoscopy. In the high-risk cohort, patients were managed the same regardless of the Cxbladder result, although the data was used to speculate on potential changes to the protocols for this type of patient. Over the course of the study and in the 35 months of the follow-up period, no cases positive for urothelial carcinoma were missed by the first Cxbladder test (although there was at least 1 false negative result in a second, follow-up round of Cxbladder testing) and no patients developed newly invasive or metastatic urothelial carcinoma. For the low-risk cohort, confirmed recurrence occurred in 3 of the patients who initially tested negative in the Cxbladder test; only 2 of those 3 patients had a follow-up, second Cxbladder test, with 1 true positive and 1 false negative (as demonstrated in the supplementary figures). There was confirmed recurrence in 3 low risk patients who tested positive with Cxbladder. For high-risk patients, 4 patients demonstrated a recurrence of urothelial carcinoma and all 4 of these patients tested positive with Cxbladder.

In 2023, Li and colleagues evaluated Cxbladder Monitor through a prospective study of 92 patients diagnosed with non-muscle invasive bladder cancer (NMIBC) from 2 different clinical sites that were ready for follow up regarding their diagnosis (primary or recurrent), a previous procedural visit, and/or other therapy (e.g., BCG instillation).⁴⁰ The study sought to triage scheduling these patients for follow-up cystoscopy through use of at home Cxbladder Monitor testing, delaying the follow-up appointment if patients received a lower risk score (<3.5) on the Monitor test. Patients with either gross hematuria or active UTI were excluded from the study. Moreover, of the 92 patients, a total of 16 were lost to follow-up, although data was still included in summary tables for these 16 patients. Of the 24 patients followed-up earlier due to a higher risk score (>3.5), 9 were found to have tumors on cystoscopy. Of the 52 patients with delayed cystoscopy due to a lower risk score, none were found to have tumor. Note that of the 66 patients with a lower risk score, 14 were not evaluated via the delayed cystoscopy for the following reasons: did not show up for cystoscopy, chose another round of Cxbladder Monitor testing instead of cystoscopy, stopped surveillance for reasons not given, or died of “unrelated” but undescribed causes. The paper also noted that all of the patients who opted out of the follow-up cystoscopy in favor of a second Cxbladder Monitor test were only found at one of the 2 sites. The authors concluded that using at-home Cxbladder Monitor testing to triage patients and allow delayed cystoscopy for patients with a lower risk score was an effective new protocol.

In 2021, Raman and colleagues developed a fourth version of the Cxbladder test, Cxbladder Resolve, utilizing 3 different patient cohorts (total of 863 patients) in the internal validation and 1 separate cohort (548 patients) in the external validation.³² In the external validation, testing was also performed with other versions of the Cxbladder test: Cxbladder Triage and Detect.

Cxbladder Resolve was designed to identify patients with a high probability of high-impact tumors (HIT), namely high-grade urothelial carcinomas, by stratifying patients into 1 of 3 categories: high priority for HIT evaluation, work-up for HIT based on physician-directed protocol (PDP) or manage by observation. In the internal validation, Cxbladder Resolve was found to have a bootstrap-adjusted estimated sensitivity of 92.4% and specificity of 93.8% for HIT; note that the overall sensitivity and specificity for all tumors during the internal validation was 91.2% and 61.0% respectively. In the external validation, Cxbladder Resolve correctly identified all HIT diagnoses and missed 3 low grade tumors, with a cumulative sensitivity of 90.0% and specificity of 96.3%. The authors also found that using a reflexive test algorithm with Cxbladder Triage, Detect, and Resolve together would correctly identify 87.6% of patients who did not need further work-up (NPV of 99.4%).³²

In 2022, Lotan and colleagues published a study describing 2 newer versions of the Cxbladder tests (enhanced Cxbladder Triage [CxbT+] and Detect [CxbD+]).³³ The enhancement (digital droplet PCR testing of urine specimens) was used to identify the presence or absence of 6 single nucleotide polymorphisms (SNPs) associated with the genes FGFR3 and TERT. These SNPs, mostly somatic (acquired) genetic variants, can be found in urothelial carcinomas, as described in the literary references provided in Lotan and colleagues’ paper. Lotan and colleagues performed an internal validation of these new biomarkers, testing urine from 2 cohorts: 344 patients from the United States and 460 patients from Singapore. The 6 SNPs were evaluated both as stand-alone tests and as enhancement to original Cxbladder Triage and Detect tests. The authors concluded that the addition of the 6 SNPs to the Cxbladder tests improved test performance, particularly specificity.

Two publications from Davidson and colleagues in 2019 and 2020 evaluated the performance of the Cxbladder Triage test when integrated into hematuria work-up protocols.^41,42 Notably, these studies were not performed or funded by Pacific Edge Diagnostics. The 2019 paper prospectively evaluated the new protocol without enacting it within the clinical setting while the 2020 paper described the outcome of a fully enacted protocol. In the 2019 study, which included 478 patients with hematuria referred to the urology practice and 73 patients with hematuria who had not been referred, the Cxbladder test correctly triaged 42 of 44 patients with urothelial malignancy; the 2 false negatives were either confirmed or suspected (no histology obtained) low grade lesions. From their cohort, the authors found that Cxbladder Triage had a sensitivity of 95% and NPV of 98%. The authors concluded “the risk of missing a significant cancer from the adoption of the theoretical pathway appears very low and clinically acceptable,” while later stating that larger studies were still needed to “prove the true clinical value of inclusion of these biomarkers in investigative pathways.” In the 2020 study, Davidson and colleagues retrospectively evaluated the clinical courses of 884 patients with hematuria who were worked-up with the new protocol and subsequently followed for a median of 21 months. The protocol identified 46 histologically confirmed urothelial carcinomas. Cxbladder Triage results included 5 false negatives, 4 of which were detected upon imaging and 1 of which was discovered in a 3 month follow up. Cxbladder Triage results also demonstrated low specificity with 39% of results being false positives. Overall, the authors found that the protocol that included Cxbladder Triage had a sensitivity of 98.1% and NPV of 99.9%. The authors concluded that their findings “add to the increasing evidence that biomarkers have a place in the assessment of hematuria, but that the results of these assays need to be supported by imaging of the bladder.”

In terms of systemic reviews and meta-analyses, 2 publications in 2015 were identified, both from Chou and colleagues, in contract with the Agency for Healthcare Research and Quality, AHRQ, U.S. Department of Health and Human Services.^43,44 The publications discussed several urinary biomarker tests including Cxbladder Detect. However, both publications only discussed a single Cxbladder study performed by O’Sullivan and colleagues in 2012.²⁹ In the shorter publication by Chou and colleagues, a systematic review and meta-analysis in the Annals of Internal Medicine, the sensitivity and specificity of Cxbladder was given a low grade for strength of evidence (as determined by study quality, precision, consistency and directness). The process of evidence assessment was covered in greater detail in the 923 page document from Chou and colleagues entitled “Emerging Approaches to Diagnosis and Treatment of Non-Muscle-Invasive Bladder Cancer;” however, the key points concerning Cxbladder were covered in the shorter publication and mostly just reiterated in the longer document.

Another systemic review and meta-analysis was published more recently in 2022 by Laukhtina and colleagues.⁴⁵ The study reviewed 5 different urinary biomarker tests (UBT) used to detect recurrent urothelial carcinoma, including Cxbladder Monitor. The authors assessed statistical values associated with each test, such as sensitivity, specificity, PPV, NPV, and accuracy. Additionally, the authors assessed the risk of bias using the Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2), using cystoscopy and histology results for reference. The study also performed network meta-analysis on the tests as compared with cytology. Two Cxbladder Monitor studies were analyzed: Koya (2020) and Lotan (2017).^39,35 At the end of the paper, the authors concluded the performances of the 5 tests “support[ed] their potential value in preventing unnecessary cystoscopies.” The authors also assessed other diagnostic tests from 4 of the 5 test companies, including Cxbladder Triage and Detect (O’Sullivan [2012] and Davidson [2019]) and concluded “there are not enough data to support their use in the initial diagnosis setting.”^29,41

Guidelines from relevant expert societies such as the American Urological Association (AUA), the Society of Urologic Oncology (SUO), and the Society of Urodynamics, Female Pelvic Medicine & Urogenital Reconstruction (SUFU) also address urine marker tests like the Cxbladder line of tests. The 2016 AUA/SUO Guidelines only recommends use of biomarkers in very limited circumstances, concluding: “At present, urinary biomarkers are insufficiently accurate to replace cystoscopy for diagnosis/surveillance, though some appear to have predictive utility for assessing response to intravesical BCG and may help interpret indeterminate cytology.”⁴⁶ Even then, recommendations to use biomarkers are based only on an expert opinion. An amendment to the AUA/SUO Guidelines mentions Cxbladder once in a section entitled “Future Directions,” stating: “Advances in sensitivity for detection of high-grade disease in a surveillance population of high-grade NMIBC patients using the CX Bladder platform have been significant.” Notably, Cxbladder is still not mentioned in recommendation statements within this 2024 amendment.²⁶ The 2020 AUA/SUFU Guidelines, were centered around evaluation of microhematuria (MH), stating: “Clinicians should not use urine cytology or urine-based tumor markers in the initial evaluation of patients with MH. (Strong Recommendation; Evidence Level: Grade C)”⁴⁷

ThyroSeq CRC, CBLPath, Inc, University of Pittsburgh Medical Center

Six peer-reviewed publications were identified evaluating the clinical validity of the ThyroSeq CRC test. The first paper, written by Yip and colleagues in 2021, represented the first assessment of the ThyroSeq CRC test and evaluated 287 patients with Bethesda VI (malignant) cytology.⁴⁸ The second paper, written by Skaugen and colleagues in 2022, assessed ThyroSeq CRC in 100 patients with Bethesda V (suspicious for malignancy) cytology.⁴⁹ The third paper, from Liu and colleagues in 2023, retrospectively assessed the 3 tier Molecular Risk Group classification system in 578 patients who had received a thyroidectomy for primary thyroid cancer and who had been subsequently tested using the ThyroSeq v3 molecular test.⁵⁰ The primary outcome measure was recurrence of the thyroid cancer following completion of the initial oncologic treatment. A recurrence event was defined as either structural recurrence or (if no structural recurrence was detected) biochemical recurrence. The fourth paper, from Chiosea and colleagues, retrospectively assessed 50,734 FNA samples, testing for molecular alterations using ThyroSeq v3 Genomic Classifier and CRC. The tests analyzed the prevalence of diagnostic, prognostic, and targetable genetic alterations in BCV-VI nodules.⁵¹ The fifth paper, by Liu and colleagues in 2023, retrospectively assessed whether molecular testing could be a surrogate for the ATA Risk Stratification System (RSS) to estimate the risk of recurrence in 945 patients who were eligible for a thyroidectomy or lobectomy for lateral neck disease and had been subsequently tested using the ThyroSeq v3 molecular test.⁵² The final study, by Schumm and colleagues in 2023, retrospectively evaluated 105 patients with Bethesda V and VI nodules. The primary outcome was structural disease recurrence, distant metastasis, and recurrence-free survival as assessed by ThyroSeq CRC.⁵³

PancraGEN- Interpace Diagnostics

In addition to the articles submitted with comments, PubMed and Google Scholar were searched for peer-reviewed, evidence-based literature that provided information regarding the analytic and clinical validity and clinical utility for the PancraGEN test. Key words used to search in combination included: PancraGEN, PathfinderTG, molecular testing, topographic genotyping, pancreatic cyst(s), pancreatic cyst fluid, solid pancreatic lesions, and KRAS and/or GNAS mutations.

Thirty-five total publications addressing the analytical validity, clinical validity, and/or clinical utility of the PancraGEN prognostic test were identified. The papers identified focused on both individuals with pancreatic cysts and with solid pancreaticobiliary lesions.

In 2006, a patent was filed for a topographic genotyping molecular analysis test, which would later become PathfinderTG and then PancraGEN. The test was designed to classify the risk of pancreatic cysts and solid pancreaticobiliary lesions when first line evaluation results were inconclusive.⁵⁴ According to the patent, the test directly measured several aspects of a specimen: DNA quality, loss of heterozygosity (LOH) in tumor suppressor genes, mutations in oncogenes (only K-ras oncogene specifically named), other less well defined genetic targets (e.g., “structural alterations in DNA”), percentage of mutated DNA per identified DNA abnormality, and “specific temporal sequence of mutation accumulation” as determined from the aforementioned percentages of mutated DNA. Altogether, these measurements would be used for “diagnosing and/or determining the prognosis of a pancreatic anomaly in a patient suffering from pancreatic cysts” and used for “determining a course of treatment for a pancreatic anomaly.”

Since the original patent, significant changes have been made to the original test’s data input and the presentation of test results. In 2012, the test results were changed from 3 categorical results (benign, statistically indolent, and aggressive) to 4 results (benign, statistically indolent, statistically higher-risk, and aggressive).⁵⁵ Since result categories are tied to specific prognostic outcomes and advise different next steps in clinical care, changing the number and type of categories changes the test output, thereby creating a new test. The most recent version of PancraGEN added analysis of the GNAS oncogene. Since mutations in GNAS would be considered a “significant molecular alteration,” testing of GNAS would potentially reclassify any specimens that had been classified based on KRAS alone. Considering the result categories and genes analyzed were both changed, the latest version of PancraGEN is a new, distinct test. The literature for older versions of PathfinderTG are not comparable to the current version of PancraGEN.

Of the 35 publications identified, 26 publications described an earlier version of the PancraGEN test that utilized less molecular data and provided fewer categorical results than the currently offered PancraGEN test.^56-81 The Agency for Healthcare Research and Quality (AHRQ) performed a technical review of an earlier version of PancraGEN.⁸⁰ The 2015 AGA Guidelines for diagnosis and management of pancreatic cysts does not name PancraGEN directly in its section about molecular testing, but instead only cites 2 papers discussing older versions of the PancraGEN/Pathfinder test.⁷³

Five of the 35 publications identified did not analyze the PancraGEN test’s primary output (categorical results) but instead evaluated specific components of the test (i.e., molecular test data).^82-86. In fact, the study by Shen and colleagues stated that it was not meant “to evaluate the scientific methods or validity of this commercially available test.” ⁸⁵

Of the 35 publications identified, there were only 4 papers that evaluated the current version of the PancraGEN including the 4 categorical results. One retrospective study addressed PancraGEN’s clinical validity and clinical utility for pancreatic cysts.⁵⁵ Two retrospective studies utilizing data from Al-Haddad and colleagues’ study addressed the clinical utility of PancraGEN for pancreatic cysts.^87,88 One paper addressed PancraGEN’s clinical validity and clinical utility for solid pancreaticobiliary lesions.⁸⁹

DecisionDx-SCC – Castle Biosciences

In addition to the articles submitted with comments, PubMed and Google Scholar were searched for peer-reviewed, evidence-based literature which provided information regarding analytic and clinical validity and clinical utility for the DecisionDx-SCC test. Key words used to search in combination included: DecisionDx, DecisionDx-SCC, Castle Biosciences, molecular testing, melanoma, skin cancer, prognostic test, SCC, cutaneous squamous cell carcinoma, cSCC, GEP test, gene expression profile, metastasis, 40-gene profile test, and formalin-fixed paraffin embedded (FFPE) tissue.

Twenty-two total publications addressing the analytical validity, clinical validity, and clinical utility of the DecisionDx-SCC prognostic test (from Castle Biosciences) were identified. Twelve identified studies were funded by, or written by employees of, Castle Biosciences. The papers identified included 1 panel review and 3 surveys of medical professionals.^90-93 Additionally, 2 papers were evidence reviews without meta-analyses.^94,95 The remaining 6 papers included 2 that addressed analytical validity, 3 cohort studies (both prospective and retrospective) that addressed clinical validity, and 1 case series that addressed clinical utility.^96-101

UroVysion fluorescence in situ hybridization (FISH) – Abbott

In addition to the articles submitted with comments, PubMed and Google Scholar were searched for peer-reviewed, evidence-based literature which provided information regarding analytic and clinical validity and clinical utility for the UroVysion FISH test. Key words used to search in combination included: UroVysion, UroVysion FISH, UroVision, UroVision FISH, UroVysion outcomes, UroVysion utility, and fluorescence in situ hybridization.

The UroVysion FISH test for bladder cancer (Abbott) is described in 23 papers. Literature addressing analytical validity, clinical validity, or clinical utility were identified. Meta-analyses, systematic reviews, and literature reviews accounted for 10 of these papers, 7 of which assessed multiple biomarkers for urothelial cancer, not just the UroVysion FISH test.^102-111 The remaining 13 papers were identified as case-control, cross-sectional, or cohort studies (either prospective or retrospective).^34,112-123 Only 2 papers were identified that addressed clinical utility, while the rest addressed analytical and clinical validity – typically evaluating outcomes such as recurrence free survival, disease free survival, and overall survival.

Colvera – Clinical Genomics

In addition to the articles submitted with comments, PubMed and Google Scholar were searched for peer-reviewed, evidence-based literature which provided information regarding analytic and clinical validity and clinical utility for the COLVERA test. Key words used to search in combination included: COLVERA, Colvera, blood test, molecular testing, colorectal cancer (CRC), CEA test, ctDNA, colorectal adenocarcinoma, real-time PCR test, DNA methylation, CRC surveillance, and BCAT1 and IKZF1.

Eleven total publications addressing the analytic validity, clinical validity, or clinical utility of the Colvera test for colorectal cancer were identified. All 11 identified studies were funded by, and written by employees of, Clinical Genomics. The papers identified included 3 validation papers and 8 cohort studies (both prospective and retrospective).^124-134 One paper addressed analytic and clinical validity, while the rest addressed clinical validity. No papers were found that addressed the clinical utility of the Colvera test.

Note that several papers not listed in this LCD were identified addressing testing of BCAT1 and IKZF1 in colorectal cancer patients; however, since these papers did not address the Colvera test itself, they were excluded from this summary and subsequent Analysis of Evidence for Colvera.

PancreaSeq Genomic Classifier, Molecular and Genomic Pathology Laboratory, University of Pittsburgh Medical Center

In addition to the articles submitted with comments, PubMed and Google Scholar were searched for peer-reviewed, evidence-based literature which provided information regarding analytic and clinical validity and clinical utility for the PancreaSeq Genomic Classifier test. Key words used to search in combination included: PancreaSeq, 22-gene panel, molecular testing, PancreaSeq Genomic Classifier, pancreatic cyst(s), pancreatic neuroendocrine tumors (PanNETs), pancreatic adenocarcinoma, DNA targeted next-generation sequencing, DNA-targeted NGS, and KRAS and/or GNAS mutations. One paper that addresses the PancreaSeq Genomic Classifier test from University of Pittsburgh Medical Center was identified.

In 2023, Nikiforova and colleagues described the development and validation of the PancreaSeq Genomic Classifier¹³⁵, an updated test developed to replace PancreaSeq. The new test was designed to “simplify the data analysis and reporting of cystic precursor neoplasms and associated advanced neoplasia”. The study utilized 108 specimens to train the test algorithm and 77 specimens to validate the test output. The researchers concluded that their test was both “accurate in predicting pancreatic cyst type” and “improved the sensitivity of current pancreatic cyst guidelines.”

Specific Lab Tests

Cxbladder (Detect, Triage, Monitor, Resolve)

The key weakness of the Cxbladder tests is found within the test design, but before discussing the flaws in test design, the concept of GEPs requires further definition.

In brief, GEPs measure the quantity of gene-specific mRNA transcripts in a specimen (e.g., how many mRNA transcripts of the gene HOXA13 are found in the urine). Since the mRNA transcripts from different genes may be identified and counted, GEPs can approximate how much transcriptional activity is occurring per gene (how much and how fast mRNA is being transcribed from the DNA at that time). In the context of urine, mRNA is found either within whole cells (e.g., desquamated urothelium) or extracellularly (e.g. from disrupted cells). Thus, in urine, a GEP result represents all mRNA from all cells within the urine and from cells lining the urinary tract. Therefore, the GEP’s measurement of mRNA in urine is an indirect assessment that represents the sum total of all transcriptional activity from both cells freely floating in the urine and cells that line the urinary tract (from kidney to urethra). As an indirect assessment, these results would be founded upon many different assumptions such as:

• There is a normal pattern of mRNA transcriptional activity at baseline that is consistent across all healthy patients
• An increase or decrease in mRNA transcription above or below baseline represents pathophysiology
• Specific patterns of abnormal changes in mRNA transcription may be ascribed to specific pathologic processes
• Each specific, abnormal pattern of mRNA transcription applies to one and only one pathologic process
• When abnormal patterns of mRNA transcription overlap, each coexisting pathologic process they represent still may be clearly identified through the patterns

Cxbladder tests are founded on the premise that differences in gene expression between urothelial cancer and non-urothelial cancer (including non-neoplastic tissue) can be measured in urine to determine if urothelial cancer is present or not present. The authors suggest that a well-designed test would be able to not only discriminate between patients with urothelial cancer (as a specific abnormal pattern of mRNA transcriptional activity) and healthy unaffected patients (normal pattern), but also between patients with urothelial cancer and patients affected by other diseases (other distinct abnormal patterns) whether non-malignant (e.g. urinary tract infection) or malignant (e.g. renal cell carcinoma). As will be shown below, the literature for development and validation of Cxbladder tests does not support the assumptions underlying their tests and does not prove the tests’ discriminating power between patients with and without TCC.

However, before discussing the Cxbladder tests, it is critical to understand the limitations of the 2008 publication from Holyoake and colleagues since the uRNA-D test was used to create the Cxbladder tests.^28,29 The main difference between uRNA-D and Cxbladder was the addition of a single gene, CXCR2, to the Cxbladder tests, creating a 5-gene expression profile.^28,29

In their 2008 paper, Holyoake and colleagues sought to answer 2 questions: First, could an mRNA expression test allow accurate identification of TCC in urine specimens; and second, could this test also differentiate between low grade and high grade TCC? The researchers proceeded to first identify candidate genes (14 genes selected out of about 26,600 genes) by comparing TCC tissue to normal tissue. Next the researchers whittled the 14 candidate genes down to 4 genes (CDC2, MDK, IGFBP5, and HOXA13) by comparing urine from patients with TCC to urine from patients without TCC. Successful and convincing execution of test development would in part require addressing known pitfalls (as described above) of mRNA expression profile tests. Holyoake and colleagues failed to prove their test overcame these pitfalls.²⁸

Firstly, in the early development phase of the test (choosing 14 genes from over 26,000), Holyoake and colleagues utilized tissue, not urine. The methodology presumed that the ureter epithelium (tissue) taken from patients with kidney cancer would supply an mRNA expression profile comparable to urine from a patient without bladder cancer. This approach discounted the differences between tissue from a single cell type (urothelium) and urine, which contains both cell-free mRNA and cell-bound mRNA, all from a variety of urothelial and non-urothelial sources (e.g., kidney and prostate gland). A more accurate approach, if using tissue to design the test, would be to compare mRNA profiles between urothelial cancer and normal bladder urothelium from the same patient to minimize the confounding differences. After test design with tissue, there would need to be confirmation that mRNA expression profiling of tissue translated to urine testing, which could be best characterized by comparing tissue profiles with urine profiles of the same patient.²⁸

Secondly, in the test finalization phase, urine from patients with TCC was compared to urine from patients with other diseases affecting the urologic tract, both malignant and non-malignant. No urine from healthy patients was used to design the final test. Moreover, the non-TCC malignancies were not identified in this paper (e.g., no diagnoses of prostate cancer or kidney cancer). Therefore, potential genes for an mRNA profile were discovered by comparing TCC tissue to benign ureter tissue and then subsequently honed to a final test design by comparing urine from patients with bladder cancer to urine from patients with other diseases (both malignant and non-malignant) without comparing to urine from healthy patients.²⁸

Thirdly, of the over 26,000 genes investigated, ultimately only 4 genes (CDC2, MDK, IGFBP5, and HOXA13) were selected. In isolation, the selected genes were not considered unique to development of urothelial carcinoma. For instance, the authors stated “TOP2A and CDC2, which are involved in DNA synthesis and cell cycle control, showed very high overexpression across the majority of tumors examined”. In fact, when selecting genes, the authors most frequently focused on the power of a gene to discriminate in 1or more aspects of their test (e.g., HOXA13 and IGFBP5 were the best genes for discriminating between Ta tumors and T1-T4 tumors), but they often failed to adequately discuss the significance of the gene itself in the development of urothelial carcinoma. In the paper’s discussion, each of the 4 selected genes were described briefly. A single literature citation each was provided for 3 of the 4 genes stating that there were no assertions CDC2, IGFBP5, and MDK were unique to urothelial carcinogenesis. Altogether, this demonstrates that the test is based on correlation not causation and is thus an indirect assessment of the presence of TCC.²⁸

Beyond these pitfalls in the development of an mRNA expression profile test, Holyoake and colleagues study introduced bias throughout their data, such as in the selection of patients, design of the test structure, and establishment of gold-standard references.²⁸

Patients included in the test finalization portion of the study all received flexible cystoscopy and all presented with symptoms concerning for urinary tract disease. No asymptomatic patients were included. This selection process demonstrates potential bias in excluding baseline, “normal”, asymptomatic controls and selecting against patients with diseases that did not rise to a level of concern requiring cystoscopy. Also, note that the patients were selected from a Japanese population at a single institution in Kyoto, potentially limiting the relevance and applicability of the test in other dissimilar populations, such as the predominantly Caucasian but still highly diverse population of USA Medicare patients. The generalizability of results from a Japanese patient population to more heterogenous populations is questionable, thereby reducing the certainty of translating these results to the United States.²⁸

A second source of bias was found in the design of uRNA-D, which was optimized by fixing the specificity of the test at 85% after collecting and reviewing the results. In this case, bias would be introduced into the estimates of test performance (e.g. overly optimistic assessment of test accuracy), thus potentially affecting the applicability of the index test for patient populations not represented by the optimization.²⁸

A third source of bias was found in the selection and interpretation of the reference standards (cystoscopic and histologic results). Very few of the patient workups for TCC and other diseases were detailed in the study. While it is very likely that other diagnostic modalities, such as radiology, were employed to diagnose non-TCC disease, the study failed to detail these workups. In fact, the types of non-TCC malignancies were not classified in this paper. Moreover, TCC itself is not a monolithic disease but rather a heterogenous cancer with many different origins that can include environmental and/or genetic etiologies. Thus, different subtypes of TCC would have different behaviors such as increased aggressiveness or increased likelihood of metastasis. This paper primarily focused on the stage and grade of disease without consideration of the other complexities within the category of TCC. Thus, the study’s selection of reference standards could have introduced bias into the accuracy, performance, and applicability of the uRNA-D test.²⁸

In summary, the 2008 paper from Holyoake and colleagues failed to convincingly prove that their 4-gene mRNA expression profile could accurately distinguish between patients with TCC and patients without TCC, and could distinguish between patients with low and high grade TCC. Subsequent literature regarding this test, or tests utilizing this study’s data, would need to address these shortfalls before accepting the accuracy and applicability of the 4-gene mRNA expression profile.²⁸

In 2012, the first paper describing the validation of a Cxbladder test was published by O’Sullivan and colleagues. The study was designed to assess whether Cxbladder and its predecessor, uRNA-D, were more sensitive in detecting bladder cancer than other tests (cytology, NMP22 BladderChek, and NMP22 ELISA) and if they were able to properly stratify positive bladder cancer specimens into low and high tumor grade. As a validation study for Cxbladder, O’Sullivan and colleagues needed to address the shortfalls of the uRNA-D test since 4 of the 5 genes used in Cxbladder were insufficiently evaluated in the Holyoake study.^28,29

As stated above, the 2008 development of uRNA-D failed to address key assumptions inherent to mRNA expression profiles. Cxbladder was developed using the same patient specimens, test design strategy, and reference standards as used in the 2008 study. Notably, O’Sullivan and colleagues did not revisit these biases, and, thus, they tacitly reintroduced the biases of the 2008 study into the development of their 2012 Cxbladder test.^28,29

Additionally, new biases were introduced in the 2012 paper via the patient population used in validating Cxbladder. The study consecutively enrolled patients from 9 urology clinics in Australia, and all subjects were evaluated using cystoscopy, cytology, uRNA-D, Cxbladder, and other bladder marker tests (NMP22 BladderChek and NMP22 ELISA). Per the study, “[p]atients were eligible … if they had a recent history of primary gross hematuria requiring further investigation for possible urological cancer, were age 45 years or older and had no history of urinary tract malignancy.”²⁹ However, patients with gross hematuria occurring under 24 hours from testing, patients with “prior genitourinary manipulation,” and patients with urinary tract infection all were excluded.²⁹ These exclusions related to blood and inflammation were particularly interesting considering that Holyoake and colleagues developed uRNA by “select[ing] markers with low expression in blood and inflammatory cells”²⁸ and O’Sullivan augmented the Cxbladder test with a fifth marker, CXCR2, “that is highly expressed in neutrophils, and is predicted to improve discrimination between patients with nonmalignant disease and those with early stage, low grade UC.”²⁹ In the O’Sullivan study, they found “[c]ontrol patients with microhematuria were more likely to have false-positive tests than those without microhematuria (p 0.002),” which begs the question as to why the mRNA content of blood would render a false positive result rather than obscuring other potentially relevant mRNA from other cells (namely, increased false negatives).²⁹ Also note that most future studies evaluating Cxbladder tests perpetuated the exclusion of patients with urinary tract infections and active gross hematuria (urine samples visually discolored by blood). Those studies that permitted testing of patients with significant inflammation of the urinary tract found that false positive rates appeared greatly increased, with one study demonstrating a false positive rate of 59% in patients with significant inflammation.^41,42 Given that malignancies are often intimately associated with inflammation, one should question whether the development of the uRNA and Cxbladder tests adequately addressed discrimination between mRNA expression profiles from tumor cells and associated inflammatory cells. Therefore, the study from O’Sullivan and colleagues does NOT prove the premise that Cxbladder effectively discriminates between mRNA expression profiles of tumor cells versus white and red blood cells.²⁹

Additionally, the exclusion of patients with a history of urinary malignancy weakens the validation of Cxbladder in this 2012 study, as well as validations for subsequent Cxbladder tests since each new Cxbladder test builds on its predecessors and often reuse patient populations. In fact, if we look at assessment of non-urothelial neoplasms throughout all major published uDNA and Cxbladder studies, we see the following:

• Holyoake 2008: 33 undefined cancers were noted²⁸
• O’Sullivan 2012: 7 other neoplasms (undefined) were noted, all concurrent with urothelial carcinomas²⁹
• Kavalieris 2015: Non-urothelial neoplasms were not discussed (study population included 517 patients from the O’Sullivan 2012 study)^29,30
• Breen 2015: Non-urothelial neoplasms were not discussed (study population included patients from the O’Sullivan 2012 study)^29,34
• Kavalieris 2017: Non-urothelial neoplasms were not discussed (same patient population as Lotan 2017)^31,35
• Lotan 2017: Non-urothelial neoplasms were not discussed (same patient population as Kavalieris 2017)^31,35
• Konety 2019: In some subpopulations, patients with history of prostate or renal cell carcinoma were excluded from the study; otherwise, non-urothelial neoplasms were not discussed (study population included patients from O’Sullivan 2012 and Kavalieris/Lotan 2017)^29,31,35,38
• Davidson 2019: Other non-bladder malignancies and neoplasms were identified (but not subclassified) in a study evaluating hematuria; notably, Cxbladder-Triage was positive in most of these other malignancies (7 of 9 total) and neoplasms (2 of 3 total)⁴¹
• Koya 2020: Non-urothelial neoplasms were not discussed³⁹
• Davidson 2020: Other non-bladder malignancies and neoplasms were identified in the study but data was not presented to allow association of these other malignancies and neoplasms with their respective positive or negative results from Cxbladder.⁴²
• Raman 2021: In some subpopulations, patients with history of prostate or renal cell carcinoma were excluded from the study; otherwise, non-urothelial neoplasms were not discussed (study population included patients from O’Sullivan 2012 and Konety 2021)^29,32,38
• Lotan 2022: In some subpopulations, patients with history of prostate or renal cell carcinoma were excluded from the study; otherwise, non-urothelial neoplasms were not discussed³³
• Li 2023: Some patients (24 of 92 patients) noted to have “other cancers”; these “other” types of cancers are not described or significantly discussed with the exception of 1 defined instance from a patient with breast cancer who missed their 9 month follow-up due to conflict with breast cancer treatment⁴⁰

There are numerous potential malignancies that can contribute to urine’s genetic composition, including but not limited to renal cell cancer, bladder cancer, and prostate cancer. Pacific Edge Diagnostics utilized the same 33 undefined malignancies to develop both the uRNA-D test and first Cxbladder test. Throughout all validations of Cxbladder tests from Pacific Edge Diagnostics, only 7 undefined, non-TCC neoplasms (described in O’Sullivan’s 2012 paper) were used.²⁹ Moreover, these 7 neoplasms were concurrent with urothelial carcinoma, and thus the validation of Cxbladder tests did not validate the tests’ ability to distinguish between TCC and non-TCC neoplasms. Highlighting this validation oversight, in a study by Davidson and colleagues in 2019 (which was not funded by Pacific Edge Diagnostics), 7 of 9 patients with malignant prostate or kidney lesions received false positive Cxbladder results.⁴¹ Therefore, currently available literature does NOT prove the premise that Cxbladder effectively discriminates between TCC and non-TCC malignancies.

Altogether, the patient exclusions found in the 2012 study create a significant bias in development and validation of the Cxbladder line of tests and indicate a failure to thoroughly assess significant confounding variables for their 5 gene expression profile.²⁹

Following these foundational studies, Pacific Edge Diagnostics sought to establish a line of Cxbladder tests with subsequent literature supporting the clinical validity and utility of the tests. However, successive studies utilizing the 5 gene expression profile still needed to establish its analytic and clinical validity since the foundational studies failed to do so. As seen above in the discussion of whether Cxbladder tests can effectively discriminate between TCC and non-TCC malignancies, successive studies failed to remedy core issues with the 5 gene expression profile. As such, the credibility and applicability of these successive studies and associated tests (including Cxbladder Detect, Triage, Monitor, and enhanced Triage) cannot be established when foundational traits of the test are in question.

When a line of tests fails to truly discriminate between the disease of interest and all other conditions, normal or pathophysiologic, there is increased concern that the tests could cause patient harm. Unsurprisingly, Cxbladder tests generally have low PPVs (down to 15-16% as seen in Konety, et al 2019 and Lotan, et al 2023) and high numbers of false positives (in Konety’s paper there were 464 false positive results as compared to 86 true positive results and in Lotan’s paper there were 110 false positive results as compared to 19 true positive results).^38,33 In fact, the majority of Cxbladder papers avoid disclosing the PPV and number of false positives of their tests. Yet, these statistics are significant in that false test results, particularly false positives, can lead to patient anxiety and distress among other procedural issues related to follow up for an inaccurate result. If numerous false positive results in Cxbladder are accepted as an inherent trait of the test, providers may not be as vigilant in closely following patients with a positive Cxbladder result after a negative cystoscopy. In addition, providers may not search for other malignancies (e.g., papillary renal cell carcinoma) as a potential cause for the “false positive” Cxbladder result.

As described in the Summary of Evidence section above, systemic reviews, meta-analyses, and even guidelines from large expert organizations like the American Urological Association generally do not support the use of Cxbladder tests in patient care. At best, the guidelines “support their potential value in preventing unnecessary cystoscopies.”^26,43-47

In conclusion, the Cxbladder line of tests all suffer from insufficient test validation, a foundational problem in which potentially confounding clinical circumstances include non-TCC neoplasms and malignancies and inflammatory conditions of the urinary tract. Cxbladder also demonstrates several population biases, including a foundational study from Holyoake in 2008 that only used Japanese patients from one location.²⁸ Most of the primary literature regarding Cxbladder test development and performance is funded, if not directly written by, the test’s parent company, Pacific Edge Diagnostics. This conflict of interest must be taken into account when reviewing these papers, particularly when there are issues not discussed in Pacific Edge Diagnostic funded papers that are subsequently addressed by non-funded studies, such as the 2019 Davidson study which identified increased false positives in patients with non-TCC malignancies.⁴¹ As a result, CxBladder tests are not reasonable and necessary to support positive outcomes in the management of bladder cancer, and are therefore, are not payable.

ThyroSeq CRC, CBLPath, Inc, University of Pittsburgh Medical Center

ThyroSeq CRC is a prognostic test for malignant cytology that predicts the 5-year likelihood of cancer recurrence (low, intermediate, or high risk) based on algorithmic synthesis of raw data from the next generation sequencing (NGS) of DNA and RNA from 112 genes. Fine needle aspirated (FNA) nodules proven to be malignant on cytology are typically surgically resected, sometimes with coincident lymph node dissections. The features of the resected cancers are then assessed on permanent pathology. Therefore, in cases where malignant cytology is identified and surgery is warranted, a prognostic test for risk of recurrence based on cytologic material (before evaluation of resected material, including assessing the lymph nodes for metastases) is premature and potentially misleading. However, ThyroSeq CRC is proposed to direct extent of surgery for Bethesda VI nodules, increasing aggressiveness of surgery for more aggressive cancers. Therefore, ThyroSeq CRC must not only supply information that is not obtained through standard clinical and pathologic procedures prior to a resection, but also provide results that are subsequently confirmed on patient follow-up after the resection. Ultimately, a prognostic test should provide information that predicts the course of a patient’s disease before therapy is implemented and thus informs future clinical management to preemptively reduce adverse outcomes. For a prognostic test to be clinically useful, it must ultimately improve patient outcomes.

In the first publication describing the evaluation of the ThyroSeq CRC test, a small population of patients (n=287) with differentiated thyroid cancer (DTC) were evaluated with the CRC prognostic algorithm and their molecular risk group (low, intermediate, high) was compared to their outcome in terms of distant metastases (DM) as identified through pathology or whole body scans with iodine-131.⁴⁸ Patients were divided into 2 groups: control (n=225) and DM within 5 years (n=62). In the control group, precise numbers of how many patients fell into each CRC risk category were not supplied by the paper. Instead, the control group was further segregated into a subcategory of propensity matched patients where each DM positive patient was compared with a control patient with similar demographic and pathologic characteristics, although the authors clearly state histologic subtype was not used to perform this propensity match. Using this propensity matching technique, comparisons were provided between the 53 DM positive patients and 55 control patients. In this subgroup comparison, the DM positive patients demonstrated more high-risk scores (low=1 patient; intermediate=17 patients; high=35 patients) than the control patients (low=28 patients; intermediate=19 patients; high=8 patients). This comparison was felt to be adequate by the authors to conclude that their “molecular profile can robustly and quite accurately stratify the risk of aggressive DTC defined as DM.”

This study had numerous limitations and drew dramatic conclusions from a very small sample size that was poorly presented by the paper.⁴⁸ The immediate issue with this study was the lack of transparency. Thyroid cancer is a complex category of malignancy that includes many different subtypes of cancer, each with a variety of behaviors depending on numerous demographic, clinical, and pathologic factors. Considerations for management of cancer patients is thus a multifactorial and interdisciplinary process that requires careful evaluation. The study from Yip and colleagues not only oversimplifies the descriptions of patient populations, but the background data for each patient is not provided to allow for objective review by their readers. We are not given crucial details such as key findings in pathology reports (mitoses, lymphovascular invasion, capsular invasion, histologic subtype of the cancer) nor the number of patients with positive lymph nodes found during resection of the cancer. Instead, the patient demographics and molecular characteristics provided include simplifications such as generalized cancer types without subclassification (Papillary, Follicular, or Oncocytic) and non-specific metastatic locations (bone, lung, “>1” and other). Additionally, the propensity matched description table (Table 2) only lists mean age at diagnosis, mean tumor size, and gender ratio.

Yip and colleagues also did not provide significant insight into why some controls (n=8) were ranked as high risk while one patient with DM was categorized as low risk.⁴⁸ The purpose of the intermediate risk category is unclear and concerningly unhelpful when the number of patients with this risk category were basically the same between propensity matched DM and control patients (n=17 versus n=19, respectively).

Ultimately, it was unclear how this test would be used in patient care.⁴⁸ Given that the test is performed on cytology before resection, the authors conjectured their test could be used to guide extent of surgery (lobectomy versus total thyroidectomy) or help direct patients to therapeutic trials. However, these potential clinical utilities were not assessed in this paper.

In the second publication evaluating ThyroSeq CRC, Skaugen and colleagues performed a single-institution retrospective cohort study assessing 128 Bethesda V (suspicious for malignancy) cytology specimens.⁴⁹ The study assessed both the ThyroSeq v3 diagnostic test as well as the ThyroSeq CRC test. For the CRC portion of the study 100 specimens were assessed, with exclusion of 5 due to a benign diagnosis upon resection and 3 excluded due to concurrent metastatic disease discovered at resection. For the remaining 92 specimens, there was a mean follow-up of 51.2 months (about 4 years). The shortest follow-up time was less than 1 month, and the longest follow-up time was 470 months (nearly 40 years). It must immediately be noted here that the ThyroSeq CRC test claims to predict a 5-year risk of DM, which means over half of the CRC tested specimens (more than 46 specimens) demonstrated potentially inadequate follow-up to assess the core 5-year prognostic claim. The importance of these follow-ups becomes even more evident when the authors drew conclusions about the prognostic power of the CRC’s 3 risk categories: high, intermediate, and low. Distant metastases were identified in 12 of the 92 specimens: 6 of 11 specimens with the high-risk result and 6 of 63 specimens with the intermediate risk result. The authors did not provide a deeper analysis of the 5 high risk specimens without DM, including no speculation as to why the test potentially misclassified these specimens. Additionally, the authors did not provide significant discussion into the meaning of the intermediate-risk result that was given to 66 of the 100 specimens tested. In the paper’s conclusions ThyroSeq CRC was again proposed as potentially helpful in deciding the extent of surgery required.

Much like the first paper, the second paper (Skaugen and colleagues) lacked data transparency, making further assessments by readers difficult.⁴⁹ While Table 3 provided patient characteristics, surgical findings, and pathologic findings, all to a much greater extent than the first paper, readers were still unable to synthesize how data categories corresponded to each other (e.g., of the patients who received lymph node dissection, what subtypes of thyroid cancer were represented).

Ultimately, Skaugen and colleagues lacked sufficient follow-up to draw significant conclusions about the accuracy of the ThyroSeq CRC results.⁴⁹ The paper, while data rich, was not transparent nor thorough enough for readers to draw their own conclusions about the validity of the test. Moreover, the conclusions given by the authors regarding the prognostic test were overly simplified, such as highlighting the presence of DMs in some patients with intermediate and high-risk results and considering this correlation to be significant when they also noted absence of DMs in patients with low-risk results. Finally, the actual use of ThyroSeq CRC in the clinical setting is still unclear based on the discussion of the paper.

In the third publication Liu and colleagues assessed their 3 tier classification system (low-risk, intermediate-risk, and high-risk for recurrence) in the context of primary thyroid cancer recurrence after a primary thyroidectomy and subsequent initial oncologic therapy.⁵⁰ Notably, the test name ThyroSeq CRC was never used in this paper, even though the 3 tier system of risk stratification appeared to be the same. This raises a concern that the classification system used in this paper may not be the same methodology as used for the marketed ThyroSeq CRC. With that caveat and for the purposes of this Analysis of Evidence, this third paper will be considered contributory to the body of literature evaluating ThyroSeq CRC.

Just from the methodology section of the publication alone, we can see immediate differences between this paper and the previous 2 papers described.^48-50 Firstly, surgical specimens were permitted in the study, not just cytology specimens. This allowance of a non-cytology specimen type (“final surgical specimens,” without specification of post resection handling of tissue, formalin fixation versus fresh-frozen preservation) in a test that was presumably designed for cytologic specimen would require a separate validation of the test for a new type of specimen. Validation for this change in pre-analytic procedure was not evidenced in this paper nor in either of the 2 prior publications. Secondly, the study was not blinded due to its retrospective nature. Thirdly, in cases where multifocal cancer was identified, only samples from the “most aggressive biology” were selected for molecular testing; however, the paper does not define what constitutes “most aggressive biology.” Fourthly, the specimens included in the study included patients with preoperative Bethesda I, II, III, and IV cytology as well as Bethesda V and VI cytology. This starkly contrasts with the inclusion criteria seen in the prior 2 studies. Overall, these methodologic differences between papers reduces the comparability of results between the 3 studies.

Data collection in this study from Liu and colleagues was also different from the previous 2 papers.^48-62 For instance, Liu and colleagues recorded several details on the surgical and post-surgical treatments of the patients. This data included types of lymph node dissection (central versus lateral and prophylactic versus therapeutic), postoperative complications (e.g., hematoma, hypercalcemia, surgical site infection), and long-term complications (such as hypocalcemia and recurrent laryngeal nerve paresis). Several of these data categories were similar to those seen in Skaugen and colleagues’ study, but differences found in Liu’s publication included post-operative details, grouping of several types of papillary thyroid cancer (such as tall cell variant) into a more general category (i.e., “Papillary, high risk"), evaluating only all-cause mortality (and not substratifying into disease specific mortality), and detailing American Joint Committee on Cancer (AJCC) prognostic stages. Note that, as mentioned above, there was a paucity of clinical and pathologic data provided for samples in the study from Yip and colleagues, and the amount of data from Liu and colleagues was far more diverse than that prior study.

The study followed up patients for a median of 19 months (Interquartile range [IQR] of 10-31 months).⁵⁰ None of the patients were followed for a total of 5 years, which means the data in this study is insufficient to substantiate the 5-year prognostication claims of the ThyroSeq CRC test.

The above analysis captures only some of the issues identified with the study from Liu and colleagues.⁵⁰ In fact, careful reading of the paper’s discussion brings up numerous other “limitations” to the study, not already described above, as identified by the authors. While the authors’ discussion remains upbeat, statements such as “how to manage the intermediate group?” draw attention to the novelty of this classification schema and the uncertainty of what and how the results can impact patient care and outcome. While the authors suggest numerous ways their classifications can affect patient management, and even suggest that they use this test within their institution to guide their decision-making, the lack of evidence demonstrating this prognostic test’s clinical utility through carefully designed studies suggests that the test currently may not be adequately studied for use in patient care. In spite of the numerous data supplied, this paper still failed to adequately evaluate the clinical validity and utility of the prognostic 3 tier system.

In the fourth publication evaluating ThyroSeq CRC, Chiosea and colleagues performed a retrospective analysis of 50,734 FNA specimens from BCIII-VI nodules.⁵¹ The samples were first analyzed using ThyroSeq v3 assay which classified results as negative or positive. All test-positive samples were then re-examined using ThyroSeq CRC to establish their result in the 3 tier classification system (low-risk, intermediate-risk, and high-risk for recurrence). From the above mentioned FNA samples, 65.3% were test-negative, and 33.9% were test-positive. Among test-positive results for follicular lesions, 73.3% had mutations, 11.3% gene fusions, and 10.8% had isolated copy number alterations. ThyroSeq Cancer Risk Classifier identified high-risk profiles in 6% of samples, more frequently in BCV-VI.

This study had multiple limitations centering around data availability and certainty of evidence. First, the study provided no confidence intervals with which to measure or examine the certainty of their data. Second, ThyroSeq CRC is used to assign risk of cancer recurrence and thus is designed to be utilized on specimens which are suspicious for malignancy or are definitively malignant (BCV and BCVI respectively). However, the vast majority of specimens included in the study were BCIII-IV (48,347 samples of 50,734 samples total), representing indeterminant cytology (atypical of undetermined significance and suspicion for follicular neoplasm respectively) that could be benign or malignant. Therefore, these BCIII-IV samples, when benign, would not require the output of the CRC test. Third, the cytologic diagnoses were rendered by local cytopathologists with different diagnostic thresholds without utilizing centralized cytopathology review. Therefore, the results may not be truly comparable depending on degrees of variability in diagnostic behavior between local cytopathologists. Fourth, the study design also did not include association of results with subsequent histologic diagnoses or clinical follow-up. Thus, the paper did not adequately evaluate the clinical validity and utility of ThyroSeq CRC.⁵¹

In the fifth publication Liu and colleagues examined whether preoperative factors are linked to incomplete response to initial therapy and if molecular testing results can be used as a substitute for the ATA Risk Stratification System (RSS) to estimate risk of recurrence.⁵² Similar to the first Liu et al study, this paper used the 3 tier classification system (low-risk, intermediate-risk, and high-risk for recurrence) in the context of recurrence through the analysis of 108 molecular permutations using previously reported methodology. As in the previous Liu study, the test name ThyroSeq CRC was never used in this paper, even though the 3 tier system of risk stratification appeared to be the same. This raises a concern that the classification system used in this paper may not be the same methodology as is used for the marketed ThyroSeq CRC. However, this paper will still be considered contributory to the body of literature evaluating ThyroSeq CRC.

A careful reading of the paper’s discussion brings up numerous limitations to the study that were identified by the authors. The study had a short follow-up with a median of 18 months. For the analysis of recurrence, unknown preoperative factors could have been used to predict recurrence that were not analyzed, such as ultrasound characteristics. The study also had possible selection bias due to the nature of the patient selection (as noted by the authors in the limitations section). Inclusion criteria consisted of consecutive patients who underwent index thyroidectomy for any clinical indication and had pathologically identified primary DTC between November 1, 2017, and October 31, 2021, were abstracted from the electronic health record. However, patients who had initial thyroid surgery earlier or elsewhere for benign disease and underwent a thyroidectomy for thyroid carcinoma during the study period, as well as patients who had previous thyroid surgery earlier or elsewhere for DTC, requiring reoperation during the study period, were not abstracted. In cases of multifocal cancer, the thyroid cancer type with the most aggressive histology was recorded, as this dictated clinical care. Additionally, the retrospective nature of the study and lack of blinding increases the concern for subjectivity in patient selection, and as a result, lowers the certainty in the clinically significance of this test in uncurated patient populations. Finally, the authors themselves noted that additional validation data is needed regarding molecular risk groups (MRGs), and until available, MRGs should not replace the gold standard method in routine thyroid cancer care.⁵²

In the final publication, a retrospective cohort study by Schumm and colleagues, patients with Bethesda V and VI nodules who underwent surgery with histopathology showing differentiated thyroid cancer were examined using ThyroSeq v3 and ThyroSeq CRC.⁵³ The structural disease persistence or recurrence, distant metastasis, and recurrence-free survival were gauged using ThyroSeq CRC molecular risk groups (low, RAS-like; intermediate, BRAF-like; high, combination of BRAF/RAS plus TERT or other high-risk alterations). Of the 105 patients, genomic alterations were found in 100 samples: 6 MRG-low, 88-MRG intermediate, and 6 MRG-high.

As with the first Liu et al study, surgical specimens were used in the study, not cytology specimens.^50,53 This allowance of a non-cytology specimen type in a test that was presumably designed for cytologic specimens would require a separate validation of the test for a new type of specimen. Validation for this change in pre-analytic procedure was not evident in this paper nor in the prior publications. Secondly, the study followed up patients for a median of 3.8 years (IQR of 3.0-4.7 years). No data was provided about how many of the of the patients were followed for a total of 5 years, which does not allow for the 5 year prognostication claims of the ThyroSeq CRC test to be validated. Third, the study was not blinded due to its retrospective nature.

Outside of the above analysis, the authors explained numerous other “limitations” to the study. The study was nonrandomized and contained a small sample size. Most patients in the MRG intermediate group underwent total thyroidectomy or underwent radioactive iodine (RAI) ablation, which may have contributed to their low risk of disease recurrence. While the authors suggest numerous ways their classifications can affect patient management, they do specifically note that long-term associations between MRGs and recurrence have yet to be established and that further studies are therefore needed before MRGs can replace the current gold standard risk stratification system recommended by guidelines. This suggests that the test currently may not be adequately studied for use in patient care and has clearly not been validated in a prognostic setting and therefore failed to adequately evaluate the clinical validity and utility of ThyroSeq CRC.⁵³

In summary, the validity of the ThyroSeq CRC test is not sufficiently supported by the 6 peer-reviewed papers identified. The 6 papers were exceptionally difficult to compare to each other due to differences in information provided, types of samples tested, and methodologies described. A number of other shortcomings were identified, including missing confidence intervals, insufficient longitudinal data, and a lack of transparency. The certainty of evidence regarding the analytic and clinical validity of ThyroSeq CRC testing is low, given these limitations. Additionally, no paper has been published specifically establishing the clinical utility of ThyroSeq CRC. Due to the inadequate quality of the papers and the insufficiency of data, this test does not have sufficient evidence to prove clinical reasonableness and necessity and will be considered non-covered in Medicare patients.

PancraGEN- Interpace Diagnostics

PancraGEN (also known as Pathfinder TG and Integrated molecular pathology [IMP]) has received multiple updates to its input data and algorithmic categorization of risk since its initial release. Comparison of early example reports to the most recent example report (available on the PancraGEN website) clearly demonstrate this evolution.^136,137 The current version of the PancraGEN report relies on algorithmic assessment of molecular data, cyst fluid test results, and radiologic findings to determine a patient’s risk for developing high grade dysplasia (HGD) and/or carcinoma. The algorithm is clearly described and diagrammed in the sample report.¹³⁷ The algorithmic stratification of risk is heavily weighted towards molecular data, with the absence of “significant molecular alterations” automatically resulting in “Benign” categorization and the presence of 2 or more “significant molecular alterations” automatically resulting in “Aggressive” categorization. The “Benign” category confers a “97% probability of benign disease over the next 3 years” and the “Aggressive” category confers a “91% probability of HGD/carcinoma”. Per the sample report, 5 “significant molecular alterations” are described¹³⁷:

1. 1. “High levels of DNA”
   2. “High clonality KRAS point mutation”
   3. “High clonality GNAS point mutation”
   4. “Single high clonality LOH tumor suppressor gene mutation”
   5. “Two or more low clonality LOW tumor suppressor gene mutations”

The current body of literature does not support the clinical validity of the PancraGEN test. Since the algorithm primarily categorizes risk via “significant molecular alterations” and the “Benign” category is defined as absence of these alterations, not testing any of the above 5 alterations would result in an underestimation of patients with potential higher risk of HGD/carcinoma. Therefore, adequate assessment of clinical validity of the current version of PancraGEN would require assessment of all 5 alterations in study populations. None of the 4 studies assessing the current version of PancraGEN fully assess all 5 alterations.^55,87-89 For example, in the 3 retrospective studies derived from National Pancreatic Cyst Registry, a significant number of patients (“468/492 IMP diagnoses”) were NOT tested for GNAS because their data was collected from earlier versions of the PancraGEN test that did not include GNAS testing.^55,87,88 Another study, from Khosravi and colleagues, addresses the 4 categorical results (simplifying them into 2 categories for the paper: low and high risk) but does not discus GNAS or clonality of identified mutations. As a result, PancraGEN studies lack the statistical integrity required to establish the clinical validity of the current version of PancraGEN.

Additionally, there are no studies supporting the clinical utility of the PancraGEN test.

First, there are no prospective studies for PancraGEN that directly evaluate its effect on patient management and outcome. The 4 studies evaluating the current version of PancraGEN are all retrospective, assessing patient populations who received PancraGEN testing as part of their clinical care; however, the assessment of PancraGEN’s effect on patient management and outcome is extrapolated from reading patient charts after the fact.

Second, cysts with a potential to develop into pancreatic cancer, like an Intraductal Papillary Mucinous Neoplasm (IPMN), can take over a decade to become malignant.^138-140 Thus, when PancraGEN categorizes a specimen as “Benign” or “Statistically Indolent” with a “97% probability of benign disease over the next 3 years”, the results may provide patients with a false sense of security and/or delay instituting a longer-term follow-up plan, potentially resulting in patient harm. Moreover, none of the studies available for PancraGEN follow up their entire patient populations for over 10 years. In fact, the "97% probability of benign disease over the next 3 years” is based on a 492 patient, 2015 study from Al-Haddad and colleagues where patients were followed-up from 23 months to 7 years and 8 months. Notably, 54% of the patients were followed up less than 3 years.

Third, current society and expert guidelines do not endorse or mention the current version of PancraGEN as necessary in the work-up of pancreatic cysts^{73,80,140,141} which further demonstrates the lack of evidence for PancraGEN’s clinical utility.

In summary, the body of literature for PancraGEN is insufficient to establish both clinical validity and clinical utility.

DecisionDx-SCC – Castle Biosciences

Cutaneous squamous cell carcinoma (cSCC) is a malignant process arising from keratinocytes in the epidermis. In patients with light pigmentation, cSCC typically develops in areas of photodamaged skin. However, in patients with darkly pigmented skin, such as those with African ancestry, the most common sites cSCC develops include the lower legs, anogenital regions, and areas of chronic inflammation or scarring, suggesting that ultraviolet radiation may not play an important etiologic factor.¹⁴² A skin biopsy is required to diagnose and provide information essential in staging and management. Once the diagnosis is made, assessing the risk of locoregional recurrence and regional or distant metastases is critical to informing management. However, there is no consensus on the specific clinicopathologic characteristics defining high-risk cSCC. Moreover, national consensus organizations such as the National Comprehensive Cancer Network (NCCN), the American Joint Committee on Cancer (AJCC), and the Brigham and Women’s Hospital (BWH) staging systems have dissimilar criteria. These staging systems are notable for their low positive predictive value (PPV) (14-38%), resulting in many patients categorized as high risk but who do not develop advanced disease.^143-147 DecisionDX-SCC is a genomic test that was developed to predict metastatic risk for SCC patients with one or more risk factors. The test classifies patients as low (Class 1), higher (Class 2A) or highest (Class 2B) biological risk of metastasis.⁹⁶

In evaluating DecisionDx – SCC, much of the analysis was focused on 3 clinical validation studies. ^96,99,100 Wysong et al developed a 40-GEP test, DecisionDX -SCC, incorporating changes in gene expression of 34 metastasis-associated genes and 6 control genes to improve risk stratification of patients with high risk for metastatic cSCC disease.⁹⁶ The intended use of this mRNA test is to assess patients with known localized, invasive cSCC disease and any single clinicopathological feature that would increase a patient's T-stage above T1 or render the patient as NCCN-high risk.

Ibrahim et al provided some additional data to support these findings, albeit using most of the same patients included in the Wysong study. Sensitivity of the 40-GEP 2B vs 1/2A result was 19%, specificity 96.9%, PPV was 52.2% and NPV was 87.2%, all of which are comparable to that reported in the Wysong study. The authors concluded that the 40-GEP test demonstrated significant prognostic value and that the risk classification was improved by integrating the 40-GEP results with clinicopathologic risk factor-based assessment.¹⁰⁰

Lastly, Aaron and colleagues published a subgroup analysis derived from the same set of patients examined by Wysong and Ibrahim; however, this time, the authors were focused on patients with SCC of the head and neck. They again concluded that the 40-GEP test enhanced the accuracy of predicting metastatic risk (p < .02) when combined with AJCC8 or BWH staging systems.⁹⁹ While these studies are notable for their consistent results, they use essentially the same set of patients thereby having no incremental effect on the certainty of their conclusions.

Insufficient evidence to prove analytic validity

DecisionDx-SCC is a laboratory-developed test (LDT) and thus not regulated by FDA. Fundamentally, DecisionDx-SCC is a GEP that analyzes 34 genes of interest (considered by Castle Biosciences to be significantly relevant to the prognosis of cSCC) and 6 control genes. Additionally, the literature has proposed several other genes (e.g., PLAUR, MMP1, MMP10, MMP13, TIMP4, and VEGFA) implicated in driving metastasis in cSCC, but are not part of the 40-GEP panel.¹⁴⁷

Although the stated intended use of DecisionDx - SCC is as a prognostic test in patients with known invasive cSCC disease, there has been no literature to prove that the test accurately predicts metastatic disease risk or provides clinically meaningful or actionable information. Though Wysong, Ibrahim, and Aaron et al concluded improved prognostic accuracy using DecisionDx-SCC in comparison to widely accepted clinicopathologic staging systems (e.g., AJCC8 and BWH), the retrospective trial design substantively diminished the certainty of their results.^96,99,100 Moreover, no prospective randomized clinical trials have been published to date showing improvement in patient-centered outcomes from using the 40-GEP test results to guide management versus SOC.

Insufficient evidence to prove clinical validity

Understanding the potential pitfalls of GEP testing is critical for understanding the reliability, performance, and accuracy of a GEP test. Eighteen of the 34 discriminant genes in the 40-GEP signature do not have an established role in cSCC biology, therefore, there is only indirect, uncorroborated evidence of their significance and how they contribute to the progression of cSCC.⁹⁶ The authors state that ‘future studies have the potential to identify how these genes promote cSCC metastasis.’⁹⁶ Moreover, the clinical validation studies published to date are notable for their observational retrospective design lending the results vulnerable to confounding variables and significant bias. As a result, clinical validation study results for DecisionDx-SCC have a low level of certainty. Moreover, all validation studies were funded by the test manufacturer compounding an elevated risk of bias and conflict of interest. Independent, prospective comparative or randomized clinical trials are necessary to enhance the quality of literature available.

Patient population is not generalizable to Medicare beneficiaries

Clinical validation studies, all using the same study population, included mostly male patients (73%) of non-Hispanic (97%), “White” ancestry (99.7%). ^96,99,100 Therefore, there is a low level of certainty that the performance of the DecisionDx-SCC GEP is applicable to the diverse Medicare patient population. For instance, given the significant differences in presentation and disease progression in patients with African ancestry, there is insufficient evidence to determine if the GEP signature and algorithm (developed in a distinctly homogenous non-Hispanic, “White” patient population) is applicable to Medicare beneficiaries with Asian, Hispanic, or African ancestry.

Insufficient evidence for clinical utility

Several clinical utility studies have been published; however, none are prospective randomized (or comparative) clinical trials demonstrating patient-centered outcomes attributable to management decisions (e.g., de-escalation of surveillance) based on DecisionDx-SCC test results. Instead, published clinical utility literature for UroVysion are physician surveys incorporating selected patient scenarios, as opposed to patients directly under their care. The results reported suffer from a high risk of bias and low level of certainty, not only related to the study design, but also conflicts of interest due to funding by the manufacturer.

Aside from the paper from Au and colleagues, papers addressing clinical utility included surveys, a panel review, and literature reviews.^90-95 These papers had several shortcomings and limitations, including, but not limited to:

• A high likelihood of selection and response bias in the surveys
• No description of survey participant recruitment methods
• An expert panel composed of Castle Bioscience employees, consultants, and researchers
• Respondents are not treating the example patients
• Survey cases are hand-picked

Based on these factors, there is insufficient evidence to determine the clinical utility for DecisionDx-SCC.

Molecular diagnostic testing for cSCC is not endorsed by specialty societies (e.g., NCCN)

There are no published society guidelines or widely accepted consensus statements published in peer-reviewed journals endorsing the use of DecisionDx-SCC to inform management of cSCC. NCCN guidelines does not include recommendation for molecular diagnostic testing to inform diagnosis, staging, or treatment.¹⁴⁸

In summary, the body of peer-reviewed literature concerning DecisionDx-SCC is insufficient to establish the analytic validity, clinical validity, and clinical utility of this test in a population of patients analogous to Medicare beneficiaries. As such, this test does not currently meet reasonable and necessary criteria for Medicare patients.

UroVysion fluorescence in situ hybridization (FISH) – Abbott

The product page on Abbott Molecular’s website states that the uFISH test “is designed to detect aneuploidy for chromosomes 3, 7, 17, and loss of the 9p21 locus via fluorescence in situ hybridization (FISH) in urine specimens from persons with hematuria suspected of having bladder cancer.”¹⁴⁹ UroVysion fluorescence in situ hybridization (uFISH) is often compared to urine cytology, and the manufacturer specifically states that the uFISH test has “greater sensitivity in detecting bladder cancer than cytology across all stages and grades.” A positive result of the test is defined by the manufacturer as 4 or more cells out of 25 showing gains for 2 or more chromosomes (3, 7, or 17) in the same cell, or 12 or more out of 25 cells having no 9p21 signals detected. However, not all bladder cancers have these alterations, and these chromosomal changes can also be seen occasionally in healthy tissues and other types of cancer^, as noted by Bonberg and colleagues (2014) and Ke and colleagues (2022).^103,112 Additionally, genomic profiles and chromosomal abnormalities can vary between low grade and high grade bladder cancer which can make the detection of low grade cancer less likely.

Clinical validation limits role due to low positive predictive value

As noted by Lavery and colleagues in 2017 and Mettman and colleagues in 2021, much of the literature assessing uFISH uses a variety of definitions for positivity.^117,119 Lavery aimed to overcome these shortcomings by using a strict definition for a positive uFISH test – which used the manufacturer’s definition along with the addition of “tetraploidy in at least 10 morphologically abnormal cells.”¹¹⁷ Tetraploidy can be seen in normal cell division and in other non-cancerous processes, so this addition was made to account for false-positive results from the uFISH test. The blinded study described in the paper found no significant difference between uFISH and urine cytology, with sensitivities of 67% and 69% and specificities of 72% and 76%, respectively. Additionally, the authors found that inclusion of the tetraploidy requirement in their definition effectively reduced false-positive rates, but also determined that some bladder cancer tumors do not have the chromosomal alterations for which uFISH assesses (30% of the tumors tested by the authors). Mettman similarly attempted to increase the accuracy of the uFISH test by including tetraploidy in their positivity definition.¹¹⁹ The authors reported considerably different results than the paper from Lavery, with sensitivity of uFISH ranging from 58-95% depending on the definition used, and a specificity using each definition of 99%. The study was specifically evaluating the test in patients suspected of having pancreatobiliary stricture malignancies, however, which could account for the differences seen between the 2 papers.

Sassa and colleagues (2019) compared the uFISH test to urine cytology in 113 patients prior to nephrouterectomy and 23 volunteers with no history of urothelial carcinoma.¹²² In cases of high-grade urothelial carcinoma (HGUC), the sensitivity, specificity, positive PPV, and NPV for detection by urinary cytology were 28.0%, 100.0%, 100.0%, and 31.6%, respectively. For uFISH, these values were 60.0%, 84.0%, 93.8%, and 41.2%, respectively. In cases of low-grade urothelial carcinoma (LGUC), however, the results were significantly worse, with sensitivities for both UroVysion and urine cytology of only 30%.

Other observational studies identified included 2 cohort studies from Nagai and colleagues (2019) and Gomella and colleagues (2017), a case-control study from Freund and colleagues (2018), and a cross-sectional study from Todenhöfer and colleagues (2014).^{113,114,121,123} Each of these studies reported similar results and limitations to the papers described above. Additionally, Breen and colleagues (2015)³⁴ evaluated uFISH in a comparative study with other tests used to detect urothelial carcinoma in urine. The other tests included Cxbladder Detect, cytology, and NMP22. The study utilized 5 cohorts of patients, only 1 of which evaluated all 4 tests for the entire cohort. Data from the 5 cohorts were evaluated and integrated, with several different imputation analyses utilized to fill in for missing test values and create a “new, imputed, comprehensive dataset.” The authors report that before imputation uFISH had a sensitivity of 40% (the lowest of the 4 tests) and a specificity of 87.3% (the second lowest of the 4 tests). Utilizing several different imputation methodologies, similar findings for comparative sensitivities and specificities were seen, leading to the conclusion that the imputed data sets were valid, with the best imputation methodology being the 3NN model. In this 3NN model, uFISH had considerably lower sensitivity than the other three tests and lower specificity than 2 of the 3 tests.

In recent years, other authors have conducted reviews and meta-analyses in order to better address the clinical validity of uFISH, and other urinary biomarkers in general. In 2022, Zheng and colleagues published a meta-analysis and review that assessed the prognostic value of uFISH to detect recurrence in the surveillance of non-muscle invasive bladder cancer (NMIBC).¹¹¹ They identified 15 studies from 2005-2019 that met their inclusion criteria and in their meta-analysis determined that the pooled sensitivity of uFISH in detecting recurrence was 68% (95% CI:58-76%) and the pooled specificity was 64% (95% CI: 53-74%).

Sciarra and colleagues (2019) conducted a systematic review to evaluate the diagnostic performance of urinary biomarkers for the initial diagnosis of bladder cancer.¹⁰⁹ The review identified 12 studies addressing uFISH, with a combined sample size of 5,033 uFISH test results. The mean sensitivity was 64.3% and the median was 64.4%, with a range of 37-100%. Additionally, the mean specificity was 88.4% and the median was 91.3%, with a range of 48-100%.

Another recent paper identified was from Soputro and colleagues (2022), who conducted a literature review and meta-analysis to evaluate the diagnostic performance of urinary biomarkers to detect bladder cancer in primary hematuria.¹¹⁰ The authors identified only 2 studies assessing uFISH that met their inclusion criteria. The pooled sensitivity and specificity of uFISH in the identified studies was 0.712 and 0.818, respectively. The authors noted that the “current diagnostic abilities of the FDA-approved biomarkers remain insufficient for their general application as a rule out test for bladder cancer diagnosis and as a triage test for cystoscopy in patients with primary hematuria.”¹²²

Sathianathen and colleagues also conducted a literature review and meta-analysis to evaluate the performance of urinary biomarkers in the evaluation of primary hematuria.¹⁰⁸ The authors were only able to identify 1 paper addressing uFISH which met their inclusion criteria, which determined that uFISH was comparable to the other biomarker tests being evaluated. However, given the fact that only 1 paper was identified which met the authors’ criteria for inclusion, the findings regarding uFISH could not be properly assessed.

The most recent meta-analysis identified was written by Papavasiliou and colleagues (2023) who assessed the diagnostic performance of urinary biomarkers potentially suitable for use in primary and community care settings.¹⁰⁷ The authors identified 10 studies addressing the diagnostic performance of uFISH between 2000 and 2022. These studies had a wide range of sensitivities (0.38-0.96) but a narrower range of specificities (0.76-0.99).

Three additional literature reviews were identified from Bulai and colleagues (2022), Miyake and colleagues (2018), and Nagai and colleagues (2021). Each of these papers noted significant issues with the literature support for these biomarkers in general, and uFISH in particular, but also lacked non-ambiguous inclusion criteria, search methods, and other necessary information to validate their assessments.^102,105,106

Clinical utility is limited to use as an adjunctive diagnostic test

UroVysion fluorescence in situ hybridization (uFISH) has also been assessed as a prognostic test for the recurrence of bladder cancer in patients and as a means of identifying recurrence in patients sooner. A paper from Guan and colleagues in 2018 evaluated the value of uFISH as a prognostic risk factor of bladder cancer recurrence and survival in patients with upper tract urothelial cancer (UTUC).¹¹⁵ One hundred and fifty-nine patients in China received a uFISH test prior to surgery and were then monitored for recurrence. While the authors did indicate that there was a relationship between uFISH results and recurrence, the results were non-significant (p=.07). Liem and colleagues (2017) conducted a prospective cohort study to evaluate whether uFISH can be used to early identify recurrence during treatment with Bacillus Calmette–Guerin (BCG).¹¹⁸ During the study, 3 bladder washouts at different time points during treatment (t0 = week 0, pre-BCG, t1 = 6 weeks following transurethral resection of bladder tumor [TURBT], t2 = 3 months following TURBT) were collected for uFISH from patients with bladder cancer that were treated with BCG. The authors found no significant association between a positive uFISH result at t0 or t1 but found that a positive uFISH result at t2 was associated with a higher risk of recurrence. Additionally, in 2020, Ikeda and colleagues published a paper that aimed to evaluate the relationship between uFISH test results following TURBT and subsequent intravesical recurrence.¹¹⁴ They indicated that uFISH test positivity was a prognostic indicator for recurrence following TURBT. However, recurrence in patients with 2 positive uFISH tests was only 33.3%, and in patients with 1 positive uFISH test (out of 2 tests total) the recurrence rate was only 16.5%.

Limited patient follow-up was a repeated weakness in papers evaluating uFISH to detect or predict recurrence. For example, the paper from Guan had a median follow-up of 27 months (range: 3-55 months), the paper from Liem had a median of 23 months of follow-up (range: 2-32 months), and the paper from Ikeda had a median follow-up of 27 months (range: 1-36.4).^115,116,118 The ranges of follow-up indicate that at least 1 patient was only followed for 1 month, and at least half of all patients had less than the median follow-up time. This limited follow-up means that cases of recurrence were likely overlooked in the studies. Even in cases where shorter follow-up may have been due to the early detection of recurrence, lack of continued follow-up could result in overlooking a patient with reduced survival following a recurrence; this additional information would be relevant to the uFISH prognostics.

Only 2 identified papers significantly addressed the clinical utility of the uFISH test: Guan (2018) and Meleth (2014).^104,115 Guan noted that they did not find any association between a positive uFISH test and survival in patients; however, as noted above, limited follow-up was a significant shortcoming of their study.¹¹⁵ Meleth and colleagues conducted a review of the available literature and were unable to find any papers that met their inclusion criteria which directly assessed patient survival, physician decision-making, or downstream health outcomes in relation to uFISH test results.¹⁰⁴ This lack of information regarding clinical utility is notable and without studies assessing for improvement in patient outcomes in a real-world setting.

It is also important to note that no studies were identified that established that uFISH was able to accurately distinguish between urothelial carcinoma and other cancers or other non-cancer urological conditions. As noted above, the specific chromosomal changes that uFISH uses to identify urothelial carcinoma have been identified in non-cancerous tissues and other types of carcinomas. This very notable gap in the identified research included a lack of details or definitions for non-urothelial cancers, of which many would feed into the urinary system, including prostate cancers, renal cancers, and metastatic or locally invasive cancers from other organs. With the knowledge that the chromosomal changes that uFISH uses to identify urothelial carcinoma can also be found in the context of other malignancies and non-malignancies, and that their identification in urine may not coincide with clinically detectable (e.g., cystoscopically visible) carcinoma, confusion could arise with false positives, especially when the PPV of uFISH tests tends to be very low. If numerous false positive results in uFISH are accepted as an inherent trait of the test, providers may not be as vigilant in closely following patients with a positive uFISH result after a negative cystoscopy. In addition, providers may not search for other malignancies as a potential cause for the “false positive” uFISH result.

AUA/SUO endorses limited role in diagnosis of bladder cancer and surveillance

The most current version of NCCN guidelines published to promote best practice for diagnosing and managing bladder cancer do not include the use of urinary biomarkers at all.²⁴ The American Urological Society and Society of Urologic Oncology^26,46 published guidelines for diagnosis and management of bladder cancer significantly limits the use of urinary biomarkers for any purpose. While AUA/SUO endorse the use of UroVysion, they applied a low strength of recommendation based on expert opinion for the specific clinical circumstances including indeterminate or nondiagnostic cytology and surveillance of recurrence after BCG administration.²⁶

Colvera – Clinical Genomics

In April 2015, Pedersen and colleagues published a validation paper in which they described a blood test which would later come to be named Colvera.¹²⁵ The test was designed to identify 2 methylated genes, namely branched-chain amino acid transaminase 1 (BCAT1) and ikaros family zinc finger protein 1 (IKZF1). Clinical Genomics had previously identified both genes as being important in the screening of colorectal cancer (CRC). Their study used methylation-specific PCR assays to measure the level of methylated BCAT1 and IKZF1 in DNA extracted from plasma obtained from colonoscopy-confirmed 144 healthy controls and 74 CRC cases. The authors found that their test was positive in 77% of cancer cases and 7.6% of controls. This study, however, failed to sufficiently address many pre-analytic variables, such as the protocols for pathologic review and the ultimate diagnosis of patients from which plasma samples were obtained.

Later that same year, another validation paper (also led by Pedersen) was published.¹²⁶ This cohort study included both prospective and retrospective methods to collect plasma samples from 2,105 volunteers and reported a test sensitivity of 66%, (95% CI: 57–74). For CRC stages I-IV respective positivity rates were 38% (95% CI: 21%–58%), 69% (95% CI: 53%–82%), 73% (95% CI: 56%–85%), and 94% (95% CI: 70%–100%). Specificity was 94% (95% CI: 92%–95%) in all 838 non-neoplastic pathology cases and 95% (95% CI: 92%–97%) in those with no colonic pathology detected (n = 450). It is important to note that case diagnosis was performed by 1 independent physician and that there were no controls involving colonoscopy or pathology procedures. The authors stated that this was due to their aim “to assess marker performance relative to outcomes determined in usual clinical practice.”

An additional validation paper was published by Murray and colleagues in 2017 which assessed both the analytic and clinical validity of the Colvera blood test.¹²⁸ The authors reported using archived samples from the previous study from Pedersen and colleagues (n=2,105 samples), but only used a subset of these archived samples (n=222 specimens, 26 with cancer).^125,126 The authors did not describe selection criteria for these samples specifically, namely, whether or not sample selection was a randomized process or why a majority of the archived specimens were not selected. Murray and colleagues found that the Colvera test had good reproducibility and repeatability with a reported sensitivity of 73.1% (95% CI: 52.2%–88.4%) and specificity of 89.3% (95% CI: 84.1%–93.2%). In addition to questions regarding sample selection, other questions were left unanswered in the paper including, but not limited to:

• Does the accuracy of the test vary in different stages of cancer?
• Does treatment (such as chemotherapy/radiation) impact the precision of the test?
• For apparent false positives, would a longer follow-up reveal them to be true positives?
• In general, would serial sampling or longitudinal data impact the precision estimates of the test?

An additional paper published in 2018 from Murray and colleagues sought to establish the clinical validity of the Colvera test.¹²⁹ In the paper, the authors tested patients post-surgery (median of 2.3 months after surgery) and followed them to establish whether or not recurrence was detected. Median follow-up for recurrence was 23.3 months, with an IQR of 14.3-29.5 months. Twenty-three participants were diagnosed with recurrence, but the Colvera test was positive in 28 participants. It should be noted that the cancer treatment varied considerably between cases, even between patients with a positive Colvera test result and those with a negative result. Only 61% of patients with a positive Colvera result completed their initial course of treatment, while 87% of patients with a negative result completed the initial course of treatment. The authors state that this was due “to either patients declining ongoing therapy, or due to comorbidities or complications precluding a full course of treatment.”¹²⁹ This could have significantly confounded the results given the higher likelihood of recurrence in a patient who did not receive a full course of treatment as opposed to patients who did receive a full course. Additionally, while the median follow-up was 23.3 months, half of the patients had a shorter follow-up than the median, and without long-term follow-up, additional cases of recurrence were likely missed.

Five other papers, all cohort studies, were identified that assessed the clinical validity of the Colvera test, in particular, Colvera’s performance compared to carcinoembryonic antigen (CEA) and/or fecal immunochemical tests (FIT).^130-134 These papers from Clinical Genomics found the sensitivity of Colvera to be 62-68% (with a wide range of 95% CIs including a study with one of 48%–84% and another of 42.4%–80.6%) and the specificity to be 87-97.9%, which is better than the sensitivity and specificity seen in CEA and FIT tests.

Young and colleagues (2016) assessed 122 patients that were being monitored for recurrent CRC (28 of whom had confirmed recurrence) to determine if Colvera or CEA was more accurate.¹³⁴ The study only obtained a blood sample 12 months prior to or 3 months following verification of a patient’s recurrence status. This method of determining test accuracy was problematic, in particular, because the follow-up lengths varied considerably between patients. In patients with confirmed recurrence, the median follow-up was 28.3 months, with an IQR of 21.9-41.0. In patients without confirmed recurrence, the median follow-up was only 17.3 months, with an IQR of 12.0-29.2. This indicates that the majority of “confirmed” cases of no recurrence were followed for less time than the median follow-up in recurrent cases. Without an adequate length of follow-up, it is certainly possible that cases of recurrence would be missed. Additionally, while the authors did report on some longitudinal data (the concordance of test results in the same patient taken at different times), that data was limited to only 30 cases out of the total 122. Of the cases that did have longitudinal data, multiple cases were reported to have false-positive test results. When combined with the insufficient follow-up already discussed, the likelihood of incorrectly identified false-positive tests increases considerably.

Musher and colleagues (2020) and Symonds and colleagues (2020) also compared Colvera to CEA for detecting recurrent CRC.^130,133 Musher, similar to the paper from Young (2016), also had short follow-up periods and insufficient longitudinal data (median follow-up was 15 months, range: 1-60 months).^130,134 Symonds (2020), however, did obtain relatively more longitudinal data and longer follow-up periods, and showed that months prior to imaging confirmation, Colvera could show a positive result.¹³³ However, without any assessment of the impact of test results on clinical outcomes, the utility of the test cannot be ascertained. Also, in the papers from Young, Musher, and Symonds, CEA sensitivity was considerably lower than normally reported in other literature (32%, 48%, and 32%, respectively).^130,133,134 While not a direct reflection on the validity or utility of Colvera, it is important to note this discrepancy since the authors were comparing Colvera to the CEA test.

Two additional cohort studies evaluating Colvera, Symonds and colleagues (2016) and Symonds and colleagues (2018), had similar findings and shortcomings as the 3 studies described above, with test sensitivities of 62% in both papers and similar study designs.^131,132

One other study evaluating Colvera, Pederson and colleagues (2023), prospectively followed 142 patients who had been treated for CRC to assess the clinical validity of Colvera in identifying the patients most likely to develop recurrence.¹²⁷ The follow-up periods were longer than previously described studies with a median follow-up of 4.2 years (IQR 2.7-6.5) overall and a median follow-up of 5.3 years (IQR 3.7-6.9) in the recurrence-free group. Of the 142 patients followed, 33 developed recurrence. Only 9 (27%) of those patients had positive Colvera results, while the remaining 24 (73%) had negative results. An additional 10 patients who had positive Colvera results did not have an identified recurrence during the follow-up period. The hazard ratio for recurrence in Colvera-positive cases was 5.7 (95% CI: 1.9–17.3, p = 0.002). The 3-year recurrence-free survival was 56.5% and 83.3% for Colvera-positive and Colvera-negative cases, respectively.

Finally, the paper from Cock and colleagues (2019) assessed the precision of both Colvera and FIT testing in the detection of sessile serrated adenomas/polyps (SSPs).¹²⁴ For this study, the authors used the same samples that were used in Symonds and colleagues (2016).¹³¹ While the paper did address pre-analytic variables and other shortcomings more sufficiently than the previous studies discussed, the results do not support the use of Colvera for the detection of SSPs. Forty-nine SSPs were identified during the colonoscopies of 1,403 participants who were also tested with the Colvera test. In those patients with SSPs, the Colvera test only had a sensitivity of 8.8%, and when combined with FIT, the sensitivity only increased to 26.5%.

Notably, there are no studies assessing patient outcomes or clinician treatment decisions in a real-world setting following a Colvera test. Without such data, clinical utility cannot be determined. One of the key factors in determining clinical utility is a test’s impact on patient outcomes. For example, a demonstration of clinical utility could be accomplished in a clinical trial where patients’ overall survival is compared between patients tested with Colvera and patients managed without this test. To date, such a trial has not been performed for Colvera.

In general, papers assessing the validity of the Colvera test for CRC have a number of shortcomings, including short follow-up time, large confidence intervals, insufficient longitudinal data, insufficient description of study methodology, and a failure to sufficiently address important pre-analytic variables. The certainty of evidence regarding the analytic and clinical validity of Colvera testing is low, given these limitations. Additionally, no paper has been published establishing the clinical utility of Colvera; a test without an improvement in patient outcomes is not clinically useful for the purposes of Medicare coverage.

In summary, the body of peer-reviewed literature concerning Colvera is insufficient to establish the analytic validity, clinical validity, and clinical utility of this test in the Medicare population. As such, this test does not currently meet reasonable and necessary criteria for Medicare patients and will not be covered.

PancreaSeq^® Genomic Classifier, Molecular and Genomic Pathology Laboratory, University of Pittsburgh Medical Center

PancreaSeq Genomic Classifier is currently unproven in both its clinical validity and clinical utility. Nikiforova and colleagues concluded in their own retrospective study utilizing archived 3-10 year old specimens:

“Prospective testing is therefore required to determine the true diagnostic performance of PancreaSeq GC and is currently underway. However, additional studies will be required to ascertain the optimal approach for PancreaSeq GC testing and how PancreaSeq GC should be incorporated into current and future pancreatic cyst guidelines.”¹³⁵

Medicare coverage of the test is thus not reasonable and necessary since research into the test’s clinical validity and utility is underway and incomplete according to the study from Nikiforova and colleagues.¹³⁵

Please refer to the related Local Coverage Article: Billing and Coding: Genetic Testing in Oncology: Specific Tests (A59125) for documentation requirements, utilization parameters and all coding information as applicable.

N/A

This bibliography presents those sources that were obtained during the development of this policy. The Contractor is not responsible for the continuing viability of Website addresses listed below.

1. US Food & Drug Administration. BEST (Biomarkers, EndpointS, and other Tools) Resource. In: National Institutes of Health, editor.: FDA-NIH Biomarker Working Group; 2016.
2. National Cancer Institute. The Genetics of Cancer. Accessed April 7, 2022. https://www.cancer.gov/about-cancer/causes-prevention/genetics.
3. National Cancer Institute. Biomarker Testing for Cancer Treatment. Accessed April 7, 2022. https://www.cancer.gov/about-cancer/treatment/types/biomarker-testing-cancer-treatment.
4. Califf RM. Biomarker definitions and their applications. Exp Biol Med (Maywood). Feb 2018;243(3):213-221. doi:10.1177/1535370217750088.
5. Centers for Medicare and Medicaid Services. National Coverage Determination (NCD): 90.2 Decision Memo. 2018. https://www.cms.gov/medicare-coverage-database/view/ncd.aspx?ncdid=372&ncdver=2&bc=0. Accessed December 19, 2024
6. National Cancer Institute. NCI Dictionary of Cancer Terms Definition of Biomarker. Accessed November 14, 2022. https://www.cancer.gov/publications/dictionaries/cancer-terms/def/biomarker.
7. Youngson RM. Collins Dictionary: Medicine. HarperCollins; 2004.
8. National Cancer Institute. NCI Dictionary of Cancer Terms Definition of Surveillance. Accessed November 15, 2022. https://www.cancer.gov/publications/dictionaries/cancer-terms/def/surveillance.
9. National Human Genome Research Institute. Talking Glossary of Genomic and Genetic Terms. Accessed March 16, 2023. https://www.genome.gov/genetics-glossary.
10. Centers for Medicare and Medicaid Services. National Coverage Determination (NCD): 90.2 Decision Memo. 2020.
11. National Cancer Institute. NCI Dictionary of Cancer Terms Definition of Circulating Tumor DNA. Accessed March 16, 2023. https://www.cancer.gov/publications/dictionaries/cancer-terms/def/circulating-tumor-dna.
12. Joseph L, Cankovic M, Caughron S, et al. The Spectrum of Clinical Utilities in Molecular Pathology Testing Procedures for Inherited Conditions and Cancer: A Report of the Association for Molecular Pathology. J Mol Diagn. Sep 2016;18(5):605-619. doi:10.1016/j.jmoldx.2016.05.007.
13. American Medical Association. Genomic Sequencing Procedures. CPT 2023 Professional Edition. American Medical Association; 2022:645.
14. Miller-Keane, O'Toole MT. Miller-Keane Encyclopedia & Dictionary of Medicine, Nursing & Allied Health, 7th Edition. 7th ed. Saunders; 2003.
15. US Food & Drug Administration. Laboratory Developed Tests. Accessed March 16, 2023. https://www.fda.gov/medical-devices/in-vitro-diagnostics/laboratory-developed-tests.
16. National Cancer Institute. NCI Dictionary of Cancer Terms Definition of Liquid Biopsy. Accessed November 15, 2022. https://www.cancer.gov/publications/dictionaries/cancer-terms/def/liquid-biopsy.
17. National Cancer Institute. NCI Dictionary of Cancer Terms Definition of Minimal Residual Disease. Accessed November 14, 2022. https://www.cancer.gov/publications/dictionaries/cancer-terms/def/minimal-residual-disease.
18. American Medical Association. Multianalyte Assays with Algorithmic Analyses (MAAAs). CPT 2023 Professional Edition. American Medical Association; 2022:649.
19. National Cancer Institute. NCI Dictionary of Cancer Terms Definition of Neoplasm. Accessed November 18, 2022. https://www.cancer.gov/publications/dictionaries/cancer-terms/def/neoplasm.
20. National Cancer Institute. NCI Dictionary of Cancer Terms Definition of Next-Generation Sequencing. Accessed November 14, 2022. https://www.cancer.gov/publications/dictionaries/cancer-terms/def/next-generation-sequencing.
21. American Medical Association. Somatic. CPT 2023 Professional Edition. American Medical Association; 2022.
22. National Cancer Institute. NCI Dictionary of Cancer Terms Definition of Tumor Mutational Burden. Accessed November 15, 2022. https://www.cancer.gov/publications/dictionaries/cancer-terms/def/tumor-mutational-burden.
23. UroVysion Bladder Cancer Kit. Summary of Safety and Effectiveness Data. U.S. Food & Drug Administration. January 24, 2005. Accessed December 19, 2024. https://www.accessdata.fda.gov/cdrh_docs/pdf3/P030052B.pdf.
24. National Comprehensive Cancer Network. Bladder Cancer. Accessed December 19, 2024. https://www.nccn.org/guidelines/guidelines-detail?category=1&id=1417.
25. Barocas DA, Boorjian SA, Alvarez RD, et al. Microhematuria: AUA/SUFU Guideline. J Urol. Oct 2020;204(4):778-786. doi:10.1097/JU.0000000000001297.
26. Holzbeierlein JM, Bixler BR, Buckley DI, Chang SS, Holmes R, James AC, Kirkby E, McKiernan JM, Schuckman AK. Diagnosis and Treatment of Non-Muscle Invasive Bladder Cancer: AUA/SUO Guideline: 2024 Amendment. J Urol. 2024 Apr;211(4):533-538. doi: 10.1097/JU.0000000000003846. Epub 2024 Jan 24. PMID: 38265030.
27. US Food & Drug Administration. Genetic Database Recognition Decision Summary for OncoKB. Accessed December 19, 2024. https://www.fda.gov/media/152847/download. 
28. Holyoake A, O'Sullivan P, Pollock R, et al. Development of a multiplex RNA urine test for the detection and stratification of transitional cell carcinoma of the bladder. Clin Cancer Res. Feb 1 2008;14(3):742-9. doi:10.1158/1078-0432.CCR-07-1672.
29. O'Sullivan P, Sharples K, Dalphin M, et al. A multigene urine test for the detection and stratification of bladder cancer in patients presenting with hematuria. J Urol. 2012;188(3):741-747. doi:10.1016/j.juro.2012.05.003.
30. Kavalieris L, O'Sullivan PJ, Suttie JM, et al. A segregation index combining phenotypic (clinical characteristics) and genotypic (gene expression) biomarkers from a urine sample to triage out patients presenting with hematuria who have a low probability of urothelial carcinoma. BMC Urol. Mar 27 2015;15:23. doi:10.1186/s12894-015-0018-5.
31. Kavalieris L, O'Sullivan P, Frampton C, et al. Performance Characteristics of a Multigene Urine Biomarker Test for Monitoring for Recurrent Urothelial Carcinoma in a Multicenter Study. J Urol. Jun 2017;197(6):1419-1426. doi:10.1016/j.juro.2016.12.010.
32. Raman JD, Kavalieris L, Konety B, et al. The Diagnostic Performance of Cxbladder Resolve, Alone and in Combination with Other Cxbladder Tests, in the Identification and Priority Evaluation of Patients at Risk for Urothelial Carcinoma. J Urol. Dec 2021;206(6):1380-1389. doi:10.1097/JU.0000000000002135.
33. Lotan Y, Raman JD, Konety B, et al. Urinary Analysis of FGFR3 and TERT Gene Mutations Enhances Performance of Cxbladder Tests and Improves Patient Risk Stratification. J Urol. Apr 2023;209(4):762-772. doi:10.1097/JU.0000000000003126.
34. Breen V, Kasabov N, Kamat AM, et al. A holistic comparative analysis of diagnostic tests for urothelial carcinoma: a study of Cxbladder Detect, UroVysion(R) FISH, NMP22(R) and cytology based on imputation of multiple datasets. BMC Med Res Methodol. May 12 2015;15:45. doi:10.1186/s12874-015-0036-8.
35. Lotan Y, O'Sullivan P, Raman JD, et al. Clinical comparison of noninvasive urine tests for ruling out recurrent urothelial carcinoma. Urol Oncol. Aug 2017;35(8):531 e15-531 e22. doi:10.1016/j.urolonc.2017.03.008.
36. Darling D, Luxmanan C, O'Sullivan P, Lough T, Suttie J. Clinical Utility of Cxbladder for the Diagnosis of Urothelial Carcinoma. Adv Ther. May 2017;34(5):1087-1096. doi:10.1007/s12325-017-0518-7.
37. Lough T, Luo Q, O'Sullivan P, et al. Clinical Utility of Cxbladder Monitor for Patients with a History of Urothelial Carcinoma: A Physician-Patient Real-World Clinical Data Analysis. Oncol Ther. Jun 2018;6(1):73-85. doi:10.1007/s40487-018-0059-5.
38. Konety B, Shore N, Kader AK, et al. Evaluation of Cxbladder and Adjudication of Atypical Cytology and Equivocal Cystoscopy. Eur Urol. 2019;76(2):238-243. doi:10.1016/j.eururo.2019.04.035.
39. Koya M, Osborne S, Chemasle C, Porten S, Schuckman A, Kennedy-Smith A. An evaluation of the real world use and clinical utility of the Cxbladder Monitor assay in the follow-up of patients previously treated for bladder cancer. BMC Urol. Feb 11 2020;20(1):12. doi:10.1186/s12894-020-0583-0.
40. Li KD, Chu CE, Patel M, Meng MV, Morgan TM, Porten SP. Cxbladder Monitor testing to reduce cystoscopy frequency in patients with bladder cancer. Urol Oncol. Jul 2023;41(7):326 e1-326 e8. doi:10.1016/j.urolonc.2023.01.009.
41. Davidson PJ, McGeoch G, Shand B. Inclusion of a molecular marker of bladder cancer in a clinical pathway for investigation of haematuria may reduce the need for cystoscopy. N Z Med J. 2019;132(1497):55-64.
42. Davidson PJ, McGeoch G, Shand B. Assessment of a clinical pathway for investigation of haematuria that reduces the need for cystoscopy. N Z Med J. Dec 18 2020;133(1527):71-82.
43. Chou R, Buckley D, Fu R, et al. AHRQ Comparative Effectiveness Reviews. Emerging Approaches to Diagnosis and Treatment of Non–Muscle-Invasive Bladder Cancer. Agency for Healthcare Research and Quality (US); 2015.
44. Chou R, Gore JL, Buckley D, et al. Urinary Biomarkers for Diagnosis of Bladder Cancer: A Systematic Review and Meta-analysis. Ann Intern Med. Dec 15 2015;163(12):922-31. doi:10.7326/M15-0997.
45. Laukhtina E, Shim SR, Mori K, et al. Diagnostic Accuracy of Novel Urinary Biomarker Tests in Non-muscle-invasive Bladder Cancer: A Systematic Review and Network Meta-analysis. Eur Urol Oncol. Dec 2021;4(6):927-942. doi:10.1016/j.euo.2021.10.003.
46. Chang SS, Boorjian SA, Chou R, et al. Diagnosis and Treatment of Non-Muscle Invasive Bladder Cancer: AUA/SUO Guideline. J Urol. Oct 2016;196(4):1021-9. doi:10.1016/j.juro.2016.06.049.
47. Woldu SL, Ng CK, Loo RK, et al. Evaluation of the New American Urological Association Guidelines Risk Classification for Hematuria. J Urol. May 2021;205(5):1387-1393. doi:10.1097/JU.0000000000001550.
48. Yip L, Gooding WE, Nikitski A, et al. Risk assessment for distant metastasis in differentiated thyroid cancer using molecular profiling: A matched case-control study. Cancer. Jun 1 2021;127(11):1779-1787. doi:10.1002/cncr.33421.
49. Skaugen JM, Taneja C, Liu JB, et al. Performance of a Multigene Genomic Classifier in Thyroid Nodules with Suspicious for Malignancy Cytology. Thyroid. Dec 2022;32(12):1500-1508. doi:10.1089/thy.2022.0282.
50. Liu JB, Ramonell KM, Carty SE, et al. Association of comprehensive thyroid cancer molecular profiling with tumor phenotype and cancer-specific outcomes. Surgery. Jan 2023;173(1):252-259. doi:10.1016/j.surg.2022.05.048.
51. Chiosea S, Hodak SP, Yip L, et al. Molecular Profiling of 50 734 Bethesda III-VI Thyroid Nodules by ThyroSeq v3: Implications for Personalized Management. J Clin Endocrinol Metab. Oct 18 2023;108(11):2999-3008. doi:10.1210/clinem/dgad220.
52. Liu JB, Baugh KA, Ramonell KM, et al. Molecular Testing Predicts Incomplete Response to Initial Therapy in Differentiated Thyroid Carcinoma Without Lateral Neck or Distant Metastasis at Presentation: Retrospective Cohort Study. Thyroid. Jun 2023;33(6):705-714. doi:10.1089/thy.2023.0060.
53. Schumm MA, Shu ML, Hughes EG, et al. Prognostic Value of Preoperative Molecular Testing and Implications for Initial Surgical Management in Thyroid Nodules Harboring Suspected (Bethesda V) or Known (Bethesda VI) Papillary Thyroid Cancer. JAMA Otolaryngol Head Neck Surg. Aug 1 2023;149(8):735-742. doi:10.1001/jamaoto.2023.1494.
54. Finkelstein S, Swalsky P, Topographic genotyping for determining the diagnosis, malignant potential, and biologic behavior of pancreatic cysts and related conditions. Patent 11143657. May 2005.
55. Al-Haddad MA, Kowalski T, Siddiqui A, et al. Integrated molecular pathology accurately determines the malignant potential of pancreatic cysts. Endoscopy. Feb 2015;47(2):136-42. doi:10.1055/s-0034-1390742.
56. Arner DM, Corning BE, Ahmed AM, et al. Molecular analysis of pancreatic cyst fluid changes clinical management. Endosc Ultrasound. Jan-Feb 2018;7(1):29-33. doi:10.4103/eus.eus_22_17.
57. Chai SM, Herba K, Kumarasinghe MP, et al. Optimizing the multimodal approach to pancreatic cyst fluid diagnosis: developing a volume-based triage protocol. Cancer Cytopathol. Feb 2013;121(2):86-100. doi:10.1002/cncy.21226.
58. Das A, Brugge W, Mishra G, Smith DM, Sachdev M, Ellsworth E. Managing incidental pancreatic cystic neoplasms with integrated molecular pathology is a cost-effective strategy. Endosc Int Open. Oct 2015;3(5):E479-86. doi:10.1055/s-0034-1392016.
59. Deftereos G, Finkelstein SD, Jackson SA, et al. The value of mutational profiling of the cytocentrifugation supernatant fluid from fine-needle aspiration of pancreatic solid mass lesions. Mod Pathol. Apr 2014;27(4):594-601. doi:10.1038/modpathol.2013.147.
60. Gillis A, Cipollone I, Cousins G, Conlon K. Does EUS-FNA molecular analysis carry additional value when compared to cytology in the diagnosis of pancreatic cystic neoplasm? A systematic review. HPB (Oxford). May 2015;17(5):377-86. doi:10.1111/hpb.12364.
61. Khalid A, Pal R, Sasatomi E, et al. Use of microsatellite marker loss of heterozygosity in accurate diagnosis of pancreaticobiliary malignancy from brush cytology samples. Gut. Dec 2004;53(12):1860-5. doi:10.1136/gut.2004.039784.
62. Khalid A, McGrath KM, Zahid M, et al. The role of pancreatic cyst fluid molecular analysis in predicting cyst pathology. Clin Gastroenterol Hepatol. Oct 2005;3(10):967-73. doi:10.1016/s1542-3565(05)00409-x.
63. Khalid A, Nodit L, Zahid M, et al. Endoscopic ultrasound fine needle aspirate DNA analysis to differentiate malignant and benign pancreatic masses. Am J Gastroenterol. Nov 2006;101(11):2493-500. doi:10.1111/j.1572-0241.2006.00740.x.
64. Khalid A, Brugge W. ACG practice guidelines for the diagnosis and management of neoplastic pancreatic cysts. Am J Gastroenterol. Oct 2007;102(10):2339-49. doi:10.1111/j.1572-0241.2007.01516.x.
65. Khalid A, Zahid M, Finkelstein SD, et al. Pancreatic cyst fluid DNA analysis in evaluating pancreatic cysts: a report of the PANDA study. Gastrointest Endosc. May 2009;69(6):1095-102. doi:10.1016/j.gie.2008.07.033.
66. Kung JS, Lopez OA, McCoy EE, Reicher S, Eysselein VE. Fluid genetic analyses predict the biological behavior of pancreatic cysts: three-year experience. Jop. Sep 28 2014;15(5):427-32. doi:10.6092/1590-8577/2426.
67. Lapkus O, Gologan O, Liu Y, et al. Determination of sequential mutation accumulation in pancreas and bile duct brushing cytology. Mod Pathol. Jul 2006;19(7):907-13. doi:10.1038/modpathol.3800545.
68. Malhotra N, Jackson SA, Freed LL, et al. The added value of using mutational profiling in addition to cytology in diagnosing aggressive pancreaticobiliary disease: review of clinical cases at a single center. BMC Gastroenterol. Aug 1 2014;14:135. doi:10.1186/1471-230X-14-135.
69. Mertz H. K-ras mutations correlate with atypical cytology and elevated CEA levels in pancreatic cystic neoplasms. Dig Dis Sci. Jul 2011;56(7):2197-201. doi:10.1007/s10620-010-1556-z.
70. Nikiforova MN, Khalid A, Fasanella KE, et al. Integration of KRAS testing in the diagnosis of pancreatic cystic lesions: a clinical experience of 618 pancreatic cysts. Mod Pathol. Nov 2013;26(11):1478-87. doi:10.1038/modpathol.2013.91.
71. Panarelli NC, Sela R, Schreiner AM, et al. Commercial molecular panels are of limited utility in the classification of pancreatic cystic lesions. Am J Surg Pathol. Oct 2012;36(10):1434-43. doi:10.1097/PAS.0b013e31825d534a.
72. Sawhney MS, Devarajan S, O'Farrel P, et al. Comparison of carcinoembryonic antigen and molecular analysis in pancreatic cyst fluid. Gastrointest Endosc. May 2009;69(6):1106-10. doi:10.1016/j.gie.2008.08.015.
73. Scheiman JM, Hwang JH, Moayyedi P. American gastroenterological association technical review on the diagnosis and management of asymptomatic neoplastic pancreatic cysts. Gastroenterology. Apr 2015;148(4):824-48 e22. doi:10.1053/j.gastro.2015.01.014.
74. Schoedel KE, Finkelstein SD, Ohori NP. K-Ras and microsatellite marker analysis of fine-needle aspirates from intraductal papillary mucinous neoplasms of the pancreas. Diagn Cytopathol. Sep 2006;34(9):605-8. doi:10.1002/dc.20511.
75. Siddiqui AA, Kowalski TE, Kedika R, et al. EUS-guided pancreatic fluid aspiration for DNA analysis of KRAS and GNAS mutations for the evaluation of pancreatic cystic neoplasia: a pilot study. Gastrointest Endosc. Apr 2013;77(4):669-70. doi:10.1016/j.gie.2012.11.009.
76. Sreenarasimhaiah J, Lara LF, Jazrawi SF, Barnett CC, Tang SJ. A comparative analysis of pancreas cyst fluid CEA and histology with DNA mutational analysis in the detection of mucin producing or malignant cysts. Jop. Mar 9 2009;10(2):163-8.
77. Talar-Wojnarowska R, Pazurek M, Durko L, et al. A comparative analysis of K-ras mutation and carcinoembryonic antigen in pancreatic cyst fluid. Pancreatology. Sep-Oct 2012;12(5):417-20. doi:10.1016/j.pan.2012.08.001.
78. Tamura K, Ohtsuka T, Date K, et al. Distinction of Invasive Carcinoma Derived From Intraductal Papillary Mucinous Neoplasms From Concomitant Ductal Adenocarcinoma of the Pancreas Using Molecular Biomarkers. Pancreas. Jul 2016;45(6):826-35. doi:10.1097/mpa.0000000000000563.
79. Trikalinos TA, Terasawa T, Raman G, Ip S, Lau J. AHRQ Technology Assessments. A Systematic Review of Loss-of-Heterozygosity based Topographic Genotyping with PathfinderTG®. Agency for Healthcare Research and Quality (US); 2010.
80. Vege SS, Ziring B, Jain R, Moayyedi P, Clinical Guidelines C, American Gastroenterology A. American gastroenterological association institute guideline on the diagnosis and management of asymptomatic neoplastic pancreatic cysts. Gastroenterology. Apr 2015;148(4):819-22; quize12-3. doi:10.1053/j.gastro.2015.01.015.
81. Winner MS, A.; Poneros, J.M.; Stavropoulos, S.,N.; Francisco, P.; Lightdale, C.J.; Allendorf, J.D.; Stevens, P.D.; Gonda, T.A. The Role of Molecular Analysis in the Diagnosis and Surveillance of Pancreatic Cystic Neoplasms. JOP. 2015;16(2):143-149. Published 2015 Mar 20. doi:10.6092/1590-8577/2941
82. Farrell JJ, Al-Haddad MA, Jackson SA, Gonda TA. Incremental value of DNA analysis in pancreatic cysts stratified by clinical risk factors. Gastrointest Endosc. Apr 2019;89(4):832-841 e2. doi:10.1016/j.gie.2018.10.049
83. Gonda TA, Viterbo D, Gausman V, et al. Mutation Profile and Fluorescence In Situ Hybridization Analyses Increase Detection of Malignancies in Biliary Strictures. Clin Gastroenterol Hepatol. Jun 2017;15(6):913-919 e1. doi:10.1016/j.cgh.2016.12.013
84. Kushnir VM, Mullady DK, Das K, et al. The Diagnostic Yield of Malignancy Comparing Cytology, FISH, and Molecular Analysis of Cell Free Cytology Brush Supernatant in Patients With Biliary Strictures Undergoing Endoscopic Retrograde Cholangiography (ERC): A Prospective Study. J Clin Gastroenterol. Oct 2019;53(9):686-692. doi:10.1097/MCG.0000000000001118
85. Shen J, Brugge WR, Dimaio CJ, Pitman MB. Molecular analysis of pancreatic cyst fluid: a comparative analysis with current practice of diagnosis. Cancer. Jun 25 2009;117(3):217-27. doi:10.1002/cncy.20027
86. Simpson RE, Cockerill NJ, Yip-Schneider MT, et al. Clinical criteria for integrated molecular pathology in intraductal papillary mucinous neoplasm: less is more. HPB (Oxford). May 2019;21(5):574-581. doi:10.1016/j.hpb.2018.09.004.
87. Kowalski T, Siddiqui A, Loren D, et al. Management of Patients With Pancreatic Cysts: Analysis of Possible False-Negative Cases of Malignancy. J Clin Gastroenterol. Sep 2016;50(8):649-57. doi:10.1097/MCG.0000000000000577.
88. Loren D, Kowalski T, Siddiqui A, et al. Influence of integrated molecular pathology test results on real-world management decisions for patients with pancreatic cysts: analysis of data from a national registry cohort. Diagn Pathol. Jan 20 2016;11:5. doi:10.1186/s13000-016-0462-x.
89. Khosravi FS, M.; Alshati, A.; Abdulameer, A.; Jackson, S. A.; Toney, N. A.; Sprague, J. L.; Narick, C. M.; Finkelstein, S. D.; & Das, A. . Mutation Profiling Impacts Clinical Decision Making and Outcomes of Patients with Solid Pancreatic Lesions Indeterminate by Cytology. Journal of the Pancreas. 2018;19(1):6-11.
90. Arron ST, Blalock TW, Guenther JM. Clinical Considerations for Integrating Gene Expression Profiling into Cutaneous Squamous Cell Carcinoma Management. J Drugs Dermatol. 2021;20(6):5s-s11. doi:10.36849/JDD.6068.
91. Work Group; Invited Reviewers; Kim JYS, Kozlow JH, Mittal B, Moyer J, Olenecki T, Rodgers P. Guidelines of care for the management of cutaneous squamous cell carcinoma. J Am Acad Dermatol. Mar, 2018;78(3):560-578. doi:10.1016/j.jaad.2017.10.007. Epub Jan 1, 2018. PMID:29331386; PMCID:PMC6652228.
92. Hooper PB, Farberg AS, Fitzgerald AL, et al. Real-World Evidence Shows Clinicians Appropriately Use the Prognostic 40-Gene Expression Profile (40-GEP) Test for High-Risk Cutaneous Squamous Cell Carcinoma (cSCC) Patients. Cancer Invest. Nov 2022;40(10):911-922. doi:10.1080/07357907.2022.2116454.
93. Litchman GH, Fitzgerald AL, Kurley SJ, Cook RW, Rigel DS. Impact of a prognostic 40-gene expression profiling test on clinical management decisions for high-risk cutaneous squamous cell carcinoma. Curr Med Res Opin. Aug 2020;36(8):1295-1300. doi:10.1080/03007995.2020.1763283.
94. Farberg AS, Fitzgerald AL, Ibrahim SF, et al. Current Methods and Caveats to Risk Factor Assessment in Cutaneous Squamous Cell Carcinoma (cSCC): A Narrative Review. Dermatol Ther (Heidelb). Feb 2022;12(2):267-284. doi:10.1007/s13555-021-00673-y
95. Newman JG, Hall MA, Kurley SJ, et al. Adjuvant therapy for high-risk cutaneous squamous cell carcinoma: 10-year review. Head Neck. Sep 2021;43(9):2822-2843. doi:10.1002/hed.26767
96. Wysong A, Newman JG, Covington KR, et al. Validation of a 40-gene expression profile test to predict metastatic risk in localized high-risk cutaneous squamous cell carcinoma. J Am Acad Dermatol. Feb 2021;84(2):361-369. doi:10.1016/j.jaad.2020.04.088.
97. Borman S, Wilkinson J, Meldi-Sholl L, et al. Analytical validity of DecisionDx-SCC, a gene expression profile test to identify risk of metastasis in cutaneous squamous cell carcinoma (SCC) patients. Diagn Pathol. Feb 25 2022;17(1):32. doi:10.1186/s13000-022-01211-w.
98. Farberg AS, Hall MA, Douglas L, et al. Integrating gene expression profiling into NCCN high-risk cutaneous squamous cell carcinoma management recommendations: impact on patient management. Curr Med Res Opin. Aug 2020;36(8):1301-1307. doi:10.1080/03007995.2020.1763284.
99. Arron ST, Wysong A, Hall MA, et al. Gene expression profiling for metastatic risk in head and neck cutaneous squamous cell carcinoma. Laryngoscope Investig Otolaryngol. Feb 2022;7(1):135-144. doi:10.1002/lio2.724.
100. Ibrahim SF, Kasprzak JM, Hall MA, et al. Enhanced metastatic risk assessment in cutaneous squamous cell carcinoma with 40-gene expression profile test. Future Oncology. 2022;18(7):833-847. doi:10.2217/fon-2021-1277.
101. Au JH, Hooper PB, Fitzgerald AL, Somani AK. Clinical Utility of the 40-Gene Expression Profile (40-GEP) Test for Improved Patient Management Decisions and Disease-Related Outcomes when Combined with Current Clinicopathological Risk Factors for Cutaneous Squamous Cell Carcinoma (cSCC): Case Series. Dermatol Ther (Heidelb). Feb 2022;12(2):591-597. doi:10.1007/s13555-021-00665-y.
102. Bulai C, Geavlete P, Ene CV, et al. Detection of Urinary Molecular Marker Test in Urothelial Cell Carcinoma: A Review of Methods and Accuracy. Diagnostics (Basel). Nov 4 2022;12(11)doi:10.3390/diagnostics12112696.
103. Ke C, Hu Z, Yang C. UroVysion(TM) Fluorescence In Situ Hybridization in Urological Cancers: A Narrative Review and Future Perspectives. Cancers (Basel). Nov 3 2022;14(21)doi:10.3390/cancers14215423.
104. Meleth S, Reeder-Hayes K, Mahima A, et al. Technology Assessment of Molecular Pathology Testing for the Estimation of Prognosis for Common Cancers. Technology Assessment. AHRQ; 2014. May 29, 2014.
105. Miyake M, Owari T, Hori S, Nakai Y, Fujimoto K. Emerging biomarkers for the diagnosis and monitoring of urothelial carcinoma. Res Rep Urol. 2018;10:251-261. doi:10.2147/RRU.S173027.
106. Nagai T, Naiki T, Etani T, et al. UroVysion fluorescence in situ hybridization in urothelial carcinoma: a narrative review and future perspectives. Transl Androl Urol. Apr 2021;10(4):1908-1917. doi:10.21037/tau-20-1207.
107. Papavasiliou E, Sills VA, Calanzani N, et al. Diagnostic Performance of Biomarkers for Bladder Cancer Detection Suitable for Community and Primary Care Settings: A Systematic Review and Meta-Analysis. Cancers (Basel). Jan 24 2023;15(3)doi:10.3390/cancers15030709.
108. Sathianathen NJ, Butaney M, Weight CJ, Kumar R, Konety BR. Urinary Biomarkers in the Evaluation of Primary Hematuria: A Systematic Review and Meta-Analysis. Bladder Cancer. Oct 29 2018;4(4):353-363. doi:10.3233/BLC-180179.
109. Sciarra A, Di Lascio G, Del Giudice F, et al. Comparison of the clinical usefulness of different urinary tests for the initial detection of bladder cancer: a systematic review. Curr Urol. Mar 2021;15(1):22-32. doi:10.1097/CU9.0000000000000012.
110. Soputro NA, Gracias DN, Dias BH, Nzenza T, O'Connell H, Sethi K. Utility of urinary biomarkers in primary haematuria: Systematic review and meta-analysis. BJUI Compass. Sep 2022;3(5):334-343. doi:10.1002/bco2.147.
111. Zheng W, Lin T, Chen Z, et al. The Role of Fluorescence In Situ Hybridization in the Surveillance of Non-Muscle Invasive Bladder Cancer: An Updated Systematic Review and Meta-Analysis. Diagnostics (Basel). Aug 19, 2022;12(8):2005. doi:10.3390/diagnostics12082005. PMID:36010354; PMCID:PMC9407231.
112. Bonberg N, Pesch B, Behrens T, et al. Chromosomal alterations in exfoliated urothelial cells from bladder cancer cases and healthy men: a prospective screening study. BMC Cancer. 2014;14:854. doi:10.1186/1471-2407-14-854.
113. Freund JE, Liem E, Savci-Heijink CD, de Reijke TM. Fluorescence in situ hybridization in 1 mL of selective urine for the detection of upper tract urothelial carcinoma: a feasibility study. Med Oncol. Nov 29 2018;36(1):10. doi:10.1007/s12032-018-1237-x.
114. Gomella LG, Mann MJ, Cleary RC, et al. Flurorescence in situ hybridization (FISH) in the diagnosis of bladder and upper tract urothelial carcinoma: the largest single-institution experience to date. The Canadian Journal of Urology. 2017;24(1):8620-8626.
115. Guan B, Du Y, Su X, et al. Positive urinary fluorescence in situ hybridization indicates poor prognosis in patients with upper tract urothelial carcinoma. Oncotarget. 2018;9(18):14652-14660.
116. Ikeda A, Kojima T, Kawai K, et al. Risk for intravesical recurrence of bladder cancer stratified by the results on two consecutive UroVysion fluorescence in situ hybridization tests: a prospective follow-up study in Japan. Int J Clin Oncol. Jun 2020;25(6):1163-1169. doi:10.1007/s10147-020-01634-9.
117. Lavery HJ, Zaharieva B, McFaddin A, Heerema N, Pohar KS. A prospective comparison of UroVysion FISH and urine cytology in bladder cancer detection. BMC Cancer. Apr 7 2017;17(1):247. doi:10.1186/s12885-017-3227-3.
118. Liem E, Baard J, Cauberg ECC, et al. Fluorescence in situ hybridization as prognostic predictor of tumor recurrence during treatment with Bacillus Calmette-Guerin therapy for intermediate- and high-risk non-muscle-invasive bladder cancer. Med Oncol. Sep 2 2017;34(10):172. doi:10.1007/s12032-017-1033-z.
119. Mettman D, Saeed A, Shold J, et al. Refined pancreatobiliary UroVysion criteria and an approach for further optimization. Cancer Med. Sep 2021;10(17):5725-5738. doi:10.1002/cam4.4043.
120. Montalbo R, Izquierdo L, Ingelmo-Torres M, et al. Urine cytology suspicious for urothelial carcinoma: Prospective follow-up of cases using cytology and urine biomarker-based ancillary techniques. Cancer Cytopathol. Jul 2020;128(7):460-469. doi:10.1002/cncy.22252.
121. Nagai T, Okamura T, Yanase T, et al. Examination of Diagnostic Accuracy of UroVysion Fluorescence In Situ Hybridization for Bladder Cancer in a Single Community of Japanese Hospital Patients. Asian Pac J Cancer Prev. Apr 29 2019;20(4):1271-1273. doi:10.31557/APJCP.2019.20.4.1271.
122. Sassa N, Iwata H, Kato M, et al. Diagnostic Utility of UroVysion Combined With Conventional Urinary Cytology for Urothelial Carcinoma of the Upper Urinary Tract. Am J Clin Pathol. Apr 2 2019;151(5):469-478. doi:10.1093/ajcp/aqy170.
123. Todenhofer T, Hennenlotter J, Witstruk M, et al. Influence of renal excretory function on the performance of urine based markers to detect bladder cancer. J Urol. Jan 2012;187(1):68-73. doi:10.1016/j.juro.2011.09.023.
124. Cock C, Anwar S, Byrne SE, et al. Low Sensitivity of Fecal Immunochemical Tests and Blood-Based Markers of DNA Hypermethylation for Detection of Sessile Serrated Adenomas/Polyps. Dig Dis Sci. Sep 2019;64(9):2555-2562. doi:10.1007/s10620-019-05569-8.
125. Pedersen SK, Baker RT, McEvoy A, et al. A two-gene blood test for methylated DNA sensitive for colorectal cancer. PLoS One. 2015;10(4):e0125041. doi:10.1371/journal.pone.0125041.
126. Pedersen SK, Symonds EL, Baker RT, et al. Evaluation of an assay for methylated BCAT1 and IKZF1 in plasma for detection of colorectal neoplasia. BMC Cancer. Oct 6 2015;15:654. doi:10.1186/s12885-015-1674-2.
127. Pedersen SK, Symonds EL, Roy AC, Cornthwaite KJ, LaPointe LC, Young GP. Detection of methylated BCAT1 and IKZF1 after curative-intent treatment as a prognostic indicator for colorectal cancer recurrence. Cancer Med. Jan 2023;12(2):1319-1329. doi:10.1002/cam4.5008.
128. Murray DH, Baker RT, Gaur S, Young GP, Pedersen SK. Validation of a Circulating Tumor-Derived DNA Blood Test for Detection of Methylated BCAT1 and IKZF1 DNA. J Appl Lab Med. Sep 1 2017;2(2):165-175. doi:10.1373/jalm.2017.023135.
129. Murray DH, Symonds EL, Young GP, et al. Relationship between post-surgery detection of methylated circulating tumor DNA with risk of residual disease and recurrence-free survival. J Cancer Res Clin Oncol. Sep 2018;144(9):1741-1750. doi:10.1007/s00432-018-2701-x.
130. Musher BL, Melson JE, Amato G, et al. Evaluation of Circulating Tumor DNA for Methylated BCAT1 and IKZF1 to Detect Recurrence of Stage II/Stage III Colorectal Cancer (CRC). Cancer Epidemiol Biomarkers Prev. Dec 2020;29(12):2702-2709. doi:10.1158/1055-9965.EPI-20-0574.
131. Symonds EL, Pedersen SK, Baker RT, et al. A Blood Test for Methylated BCAT1 and IKZF1 vs. a Fecal Immunochemical Test for Detection of Colorectal Neoplasia. Clin Transl Gastroenterol. Jan 14 2016;7(1):e137. doi:10.1038/ctg.2015.67.
132. Symonds EL, Pedersen SK, Murray DH, et al. Circulating tumour DNA for monitoring colorectal cancer-a prospective cohort study to assess relationship to tissue methylation, cancer characteristics and surgical resection. Clin Epigenetics. 2018;10:63. doi:10.1186/s13148-018-0500-5.
133. Symonds EL, Pedersen SK, Murray D, et al. Circulating epigenetic biomarkers for detection of recurrent colorectal cancer. Cancer. Apr 1 2020;126(7):1460-1469. doi:10.1002/cncr.32695.
134. Young GP, Pedersen SK, Mansfield S, et al. A cross-sectional study comparing a blood test for methylated BCAT1 and IKZF1 tumor-derived DNA with CEA for detection of recurrent colorectal cancer. Cancer Med. Oct 2016;5(10):2763-2772. doi:10.1002/cam4.868.
135. Nikiforova MN, Wald AI, Spagnolo DM, et al. A Combined DNA/RNA-based Next-Generation Sequencing Platform to Improve the Classification of Pancreatic Cysts and Early Detection of Pancreatic Cancer Arising From Pancreatic Cysts. Ann Surg. Oct 1 2023;278(4):e789-e797. doi:10.1097/SLA.0000000000005904
136. Toll AD, Bibbo M. The Added Value of Molecular Testing in Small Pancreatic Cysts. Department of Pathology, Anatomy, and Cell Biology Faculty Papers: Thomas Jefferson University; 2010.
137. PancrGEN Official website. Accessed June 11, 2024. https://pancragen.com/.
138. Lennon AM, Wolfgang CL, Canto MI, et al. The early detection of pancreatic cancer: what will it take to diagnose and treat curable pancreatic neoplasia? Cancer Res. Jul 1 2014;74(13):3381-9. doi:10.1158/0008-5472.CAN-14-0734.
139. Lennon AM, Ahuja N, Wolfgang CL. AGA Guidelines for the Management of Pancreatic Cysts. Gastroenterology. Sep 2015;149(3):825. doi:10.1053/j.gastro.2015.05.062.
140. Gardner TB, Park WG, Allen PJ. Diagnosis and Management of Pancreatic Cysts. Gastroenterology. Mar 3 2024;doi:10.1053/j.gastro.2024.02.041.
141. National Comprehensive Cancer Network. Pancreatic Adenocarcinoma. Accessed December 19, 2024. https://www.nccn.org/guidelines/guidelines-detail?category=1&id=1455.
142. Gloster Jr. HM, Neal K. Skin cancer in skin of color. J Am Acad Dermatol. 2006;55(5):741-60. doi:10.1016/j.jaad.2005.08.063.
143. Ruiz ES, Karia PS, Besaw R, Schmults CD. Performance of the American Joint Committee on Cancer Staging Manual, 8th Edition vs the Brigham and Women's Hospital Tumor Classification System for Cutaneous Squamous Cell Carcinoma. JAMA Dermatol. Jul 1 2019;155(7):819-825. doi:10.1001/jamadermatol.2019.0032.
144. Karia PS, Morgan FC, Califano JA, Schmults CD. Comparison of Tumor Classifications for Cutaneous Squamous Cell Carcinoma of the Head and Neck in the 7th vs 8th Edition of the AJCC Cancer Staging Manual. JAMA Dermatol. 2018;154(2):175-181. doi:doi:10.1001/jamadermatol.2017.3960.
145. Jambusaria-Pahlajani A, Kanetsky PA, Karia PS, et al. Evaluation of AJCC tumor staging for cutaneous squamous cell carcinoma and a proposed alternative tumor staging system. JAMA Dermatol. Apr 2013;149(4):402-10. doi:10.1001/jamadermatol.2013.2456.
146. Karia PS, Jambusaria-Pahlajani A, Harrington DP, Murphy GF, Qureshi AA, Schmults CD. Evaluation of American Joint Committee on Cancer, International Union Against Cancer, and Brigham and Women's Hospital tumor staging for cutaneous squamous cell carcinoma. J Clin Oncol. Feb 1 2014;32(4):327-34. doi:10.1200/JCO.2012.48.5326.
147. Minaei E, Mueller SA, Ashford B, Thind AS, Mitchell J, Perry JR, Genenger B, Clark JR, Gupta R, Ranson M. Cancer Progression Gene Expression Profiling Identifies the Urokinase Plasminogen Activator Receptor as a Biomarker of Metastasis in Cutaneous Squamous Cell Carcinoma. Front Oncol. 2022 Apr 11;12:835929. doi: 10.3389/fonc.2022.835929. PMID: 35480116; PMCID: PMC9035872.
148. National Comprehensive Cancer Network. Squamous Cell Skin Cancer. Accessed December 19, 2024. https://www.nccn.org/guidelines/guidelines-detail?category=1&id=1465.
149. Abbott Urovysion Bladder Cancer Kit. Accessed December 19, 2024. https://www.molecular.abbott/us/en/products/oncology/urovysion-bladder-cancer-kit.
150. Abdelhakam DA, Hanna H, Nassar A. Oncotype DX and Prosigna in breast cancer patients: A comparison study. Cancer Treat Res Commun. 2021;26:100306. doi:10.1016/j.ctarc.2021.100306.
151. Acanda De La Rocha AM, Fader M, Coats ER, et al. Clinical Utility of Functional Precision Medicine in the Management of Recurrent/Relapsed Childhood Rhabdomyosarcoma. JCO Precis Oncol. 2021;5doi:10.1200/PO.20.00438.
152. Ahola R, Sand J, Laukkarinen J. Centralization of Pancreatic Surgery Improves Results: Review. Scand J Surg. Mar 2020;109(1):4-10. doi:10.1177/1457496919900411.
153. AIM Specialty Health. Clinical Appropriateness Guidelines Specialty Laboratory Medicine Appropriate Use Criteria. AIM Specialty Health; 2021.
154. Al Saidi SS, Al Riyami NB, Al Marhoon MS, et al. Validity of Prostate Health Index and Percentage of [-2] Pro-Prostate-Specific Antigen as Novel Biomarkers in the Diagnosis of Prostate Cancer: Omani Tertiary Hospitals Experience. Oman Med J. Jul 2017;32(4):275-283. doi:10.5001/omj.2017.55.
155. Alam M, Armstrong A, Baum C, et al. Guidelines of care for the management of cutaneous squamous cell carcinoma. J Am Acad Dermatol. Mar 2018;78(3):560-578. doi:10.1016/j.jaad.2017.10.007.
156. Alam S, Tortora J, Staff I, McLaughlin T, Wagner J. Prostate cancer genomics: comparing results from three molecular assays. Can J Urol. 2019;26(3):9758-9762.
157. Albain KS, Barlow WE, Shak S, et al. Prognostic and predictive value of the 21-gene recurrence score assay in postmenopausal women with node-positive, oestrogen-receptor-positive breast cancer on chemotherapy: a retrospective analysis of a randomised trial. Lancet Oncol. Jan 2010;11(1):55-65. doi:10.1016/S1470-2045(09)70314-6.
158. Albala D, Kemeter MJ, Febbo PG, et al. Health Economic Impact and Prospective Clinical Utility of Oncotype DX(R) Genomic Prostate Score. Rev Uro 2016;18(3):123-132. doi:10.3909/riu0725.
159. Albitar M, Zhang H, Goy A, et al. Determining clinical course of diffuse large B-cell lymphoma using targeted transcriptome and machine learning algorithms. Blood Cancer J. Feb 1 2022;12(2):25. doi:10.1038/s41408-022-00617-5.
160. Altman AM, Kizy S, Yuan J, et al. Distribution of 21-Gene Recurrence Scores in Male Breast Cancer in the United States. Ann Surg Oncol. Aug 2018;25(8):2296-2302. doi:10.1245/s10434-018-6566-7.
161. American Cancer Society. Cancer Statistics Center Bladder Cancer. https://cancerstatisticscenter.cancer.org/#%21/cancer-site/Urinary%20bladder.
162. American College of Obstetricians and Gynecologists. Committee on Practice Bulletins-Gynecology. Practice Bulletin No. 174: Evaluation and Management of Adnexal Masses. Obstet Gynecol. 2016;128(5):e210-e226. doi:10.1097/AOG.0000000000001768. 
163. Amin MB, Greene FL, Edge SB, et al. The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population-based to a more "personalized" approach to cancer staging. CA Cancer J Clin. Mar 2017;67(2):93-99. doi:10.3322/caac.21388.
164. Arber DA, Orazi A, Hasserjian RP, et al. International Consensus Classification of Myeloid Neoplasms and Acute Leukemias: integrating morphologic, clinical, and genomic data. Blood. Sep 15 2022;140(11):1200-1228. doi:10.1182/blood.2022015850.
165. Arbustini E, Behr ER, Carrier L, et al. Interpretation and actionability of genetic variants in cardiomyopathies: a position statement from the European Society of Cardiology Council on cardiovascular genomics. Eur Heart J. May 21 2022;43(20):1901-1916. doi:10.1093/eurheartj/ehab895.
166. Arrangoiz R, De Llano JG, Mijares MF, et al. Molecular Diagnosis of Thyroid Nodules and Its Future Implications for the Management of Thyroid Cancer. International Journal of Otolaryngology and Head & Neck Surgery. 2021;10(05):398-418. doi:10.4236/ijohns.2021.105037.
167. Auprich M, Bjartell A, Chun FK, et al. Contemporary role of prostate cancer antigen 3 in the management of prostate cancer. Eur Urol. Nov 2011;60(5):1045-54. doi:10.1016/j.eururo.2011.08.003.
168. Azzam D, Volmar C-H, Hassan A-A, et al. A Patient-Specific Ex Vivo Screening Platform for Personalized Acute Myeloid Leukemia (AML) Therapy. Blood. 2015;126(23):1352-1352. doi:10.1182/blood.V126.23.1352.1352.
169. Badani KK, Kemeter MJ, Febbo PG, et al. The Impact of a Biopsy Based 17-Gene Genomic Prostate Score on Treatment Recommendations in Men with Newly Diagnosed Clinically Prostate Cancer Who are Candidates for Active Surveillance. Urol Pract. Jul 2015;2(4):181-189. doi:10.1016/j.urpr.2014.10.010.
170. Baehner FL. The analytical validation of the Oncotype DX Recurrence Score assay. Ecancermedicalscience. 2016;10:675. doi:10.3332/ecancer.2016.675.
171. Bai Q, Lv H, Bao L, et al. Invasive Breast Cancer with HER2 >/=4.0 and <6.0: Risk Classification and Molecular Typing by a 21-Gene Expression Assay and MammaPrint Plus BluePrint Testing. Breast Cancer (Dove Med Press). 2023;15:563-575. doi:10.2147/BCTT.S420738.
172. Baqar AR, Wilkins S, Staples M, Angus Lee CH, Oliva K, McMurrick P. The role of preoperative CEA in the management of colorectal cancer: A cohort study from two cancer centres. Int J Surg. Apr 2019;64:10-15. doi:10.1016/j.ijsu.2019.02.014.
173. Bear HD, Wan W, Robidoux A, et al. Using the 21-gene assay from core needle biopsies to choose neoadjuvant therapy for breast cancer: A multicenter trial. J Surg Oncol. Jun 2017;115(8):917-923. doi:10.1002/jso.24610.
174. Becerra AZ, Probst CP, Tejani MA, et al. Evaluating the Prognostic Role of Elevated Preoperative Carcinoembryonic Antigen Levels in Colon Cancer Patients: Results from the National Cancer Database. Ann Surg Oncol. May 2016;23(5):1554-61. doi:10.1245/s10434-015-5014-1.
175. Berg JS, Foreman AK, O'Daniel JM, et al. A semiquantitative metric for evaluating clinical actionability of incidental or secondary findings from genome-scale sequencing. Genet Med. May 2016;18(5):467-75. doi:10.1038/gim.2015.104.
176. Bhatti I, Patel M, Dennison AR, Thomas MW, Garcea G. Utility of postoperative CEA for surveillance of recurrence after resection of primary colorectal cancer. Int J Surg. Apr 2015;16(Pt A):123-128. doi:10.1016/j.ijsu.2015.03.002.
177. Bishoff JT, Freedland SJ, Gerber L, et al. Prognostic utility of the cell cycle progression score generated from biopsy in men treated with prostatectomy. J Urol. Aug 2014;192(2):409-14. doi:10.1016/j.juro.2014.02.003.
178. Blick CG, Nazir SA, Mallett S, et al. Evaluation of diagnostic strategies for bladder cancer using computed tomography (CT) urography, flexible cystoscopy and voided urine cytology: results for 778 patients from a hospital haematuria clinic. BJU Int. Jul 2012;110(1):84-94. doi:10.1111/j.1464-410X.2011.10664.x.
179. Blom EF, Ten Haaf K, Arenberg DA, de Koning HJ. Disparities in Receiving Guideline-Concordant Treatment for Lung Cancer in the United States. Ann Am Thorac Soc. Feb 2020;17(2):186-194. doi:10.1513/AnnalsATS.201901-094OC.
180. Bohmann K, Hennig G, Rogel U, et al. RNA extraction from archival formalin-fixed paraffin-embedded tissue: a comparison of manual, semiautomated, and fully automated purification methods. Clin Chem. Sep 2009;55(9):1719-27. doi:10.1373/clinchem.2008.122572.
181. Boland MR, Al-Maksoud A, Ryan EJ, et al. Value of a 21-gene expression assay on core biopsy to predict neoadjuvant chemotherapy response in breast cancer: systematic review and meta-analysis. Br J Surg. Jan 27 2021;108(1):24-31. doi:10.1093/bjs/znaa048.
182. Brand TC, Zhang N, Crager MR, et al. Patient-specific Meta-analysis of 2 Clinical Validation Studies to Predict Pathologic Outcomes in Prostate Cancer Using the 17-Gene Genomic Prostate Score. Urology. Mar 2016;89:69-75. doi:10.1016/j.urology.2015.12.008.
183. Bristow RE, Chang J, Ziogas A, Randall LM, Anton-Culver H. High-volume ovarian cancer care: survival impact and disparities in access for advanced-stage disease. Gynecol Oncol. Feb 2014;132(2):403-10. doi:10.1016/j.ygyno.2013.12.017.
184. Bristow RE, Powell MA, Al-Hammadi N, et al. Disparities in ovarian cancer care quality and survival according to race and socioeconomic status. J Natl Cancer Inst. Jun 5 2013;105(11):823-32. doi:10.1093/jnci/djt065.
185. Brooks MA, Thomas L, Magi-Galluzzi C, et al. GPS Assay Association With Long-Term Cancer Outcomes: Twenty-Year Risk of Distant Metastasis and Prostate Cancer-Specific Mortality. JCO Precis Oncol. 2021;5doi:10.1200/PO.20.00325.
186. Brugge WR, Lauwers GY, Sahani D, Fernandez-del Castillo C, Warshaw AL. Cystic Neoplasms of the Pancreas. The New England Journal of Medicine. 2004;351(12):1218-26.
187. Cancer Genome Atlas Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature. Jul 18 2012;487(7407):330-7. doi:10.1038/nature11252.
188. Canfield S, Kemeter MJ, Febbo PG, Hornberger J. Balancing Confounding and Generalizability Using Observational, Real-world Data: 17-gene Genomic Prostate Score Assay Effect on Active Surveillance. Rev Urol. 2018;20(2):69-76. doi:10.3909/riu0799.
189. Canter DJ, Branch C, Shelnutt J, et al. The 17-Gene Genomic Prostate Score Assay Is Prognostic for Biochemical Failure in Men With Localized Prostate Cancer After Radiation Therapy at a Community Cancer Center. Adv Radiat Oncol. Jul-Aug 2023;8(4):101193. doi:10.1016/j.adro.2023.101193.
190. Canueto J, Burguillo J, Moyano-Bueno D, et al. Comparing the eighth and the seventh editions of the American Joint Committee on Cancer staging system and the Brigham and Women's Hospital alternative staging system for cutaneous squamous cell carcinoma: Implications for clinical practice. J Am Acad Dermatol. Jan 2018;80(1):106-113 e2. doi:10.1016/j.jaad.2018.06.060.
191. Castle Biosciences. DecisionDx-UM Final Report Sample. Accessed March 4, 2023. https://castlebiosciences.com/wp-content/uploads/2015/01/DecisionDx-UM-Sample-Report.pdf.
192. Caudill J, Thomas JE, Burkhart CG. The risk of metastases from squamous cell carcinoma of the skin. Int J Dermatol. Apr 2023;62(4):483-486. doi:10.1111/ijd.16164.
193. cBioPortal for Cancer Genomics. cBioPortal for Cancer Genomics. Accessed March 16, 2023. https://www.cbioportal.org/.
194. Cedars BE, Washington SL, 3rd, Cowan JE, et al. Stability of a 17-Gene Genomic Prostate Score in Serial Testing of Men on Active Surveillance for Early Stage Prostate Cancer. J Urol. Oct 2019;202(4):696-701. doi:10.1097/JU.0000000000000271.
195. Centers for Medicare and Medicaid Services. Medicare Benefit Policy Manual. Chapter 15, Section 50.4.5 - Off-Label Use of Drugs and Biologicals in an Anti-Cancer Chemotherapeutic Regimen.
196. Chae EY, Moon WK, Kim HH, et al. Association between Ultrasound Features and the 21-Gene Recurrence Score Assays in Patients with Oestrogen Receptor-Positive, HER2-Negative, Invasive Breast Cancer. PLoS One. 2016;11(6):e0158461. doi:10.1371/journal.pone.0158461.
197. Chai CA, Yeoh WS, Rajandram R, et al. Comparing CxBladder to Urine Cytology as Adjunct to Cystoscopy in Surveillance of Non-muscle Invasive Bladder Cancer-A Pilot Study. Front Surg. 2021;8:659292. doi:10.3389/fsurg.2021.659292.
198. Chamie K, Litwin MS, Bassett JC, et al. Recurrence of high-risk bladder cancer: a population-based analysis. Cancer. Sep 1 2013;119(17):3219-27. doi:10.1002/cncr.28147.
199. Clark TG, Bradburn MJ, Love SB, Altman DG. Survival analysis part I: basic concepts and first analyses. Br J Cancer. Jul 21 2003;89(2):232-8. doi:10.1038/sj.bjc.6601118.
200. Clayman GL, Lee JJ, Holsinger FC, et al. Mortality risk from squamous cell skin cancer. J Clin Oncol. Feb 1 2005;23(4):759-65. doi:10.1200/JCO.2005.02.155.
201. Cooperberg MR, Davicioni E, Crisan A, Jenkins RB, Ghadessi M, Karnes RJ. Combined value of validated clinical and genomic risk stratification tools for predicting prostate cancer mortality in a high-risk prostatectomy cohort. Eur Urol. Feb 2015;67(2):326-33. doi:10.1016/j.eururo.2014.05.039.
202. Cooperberg MR, Simko JP, Cowan JE, et al. Validation of a cell-cycle progression gene panel to improve risk stratification in a contemporary prostatectomy cohort. J Clin Oncol. Apr 10 2013;31(11):1428-34. doi:10.1200/JCO.2012.46.4396.
203. Cowan B, Klein E, Jansz K, et al. Longitudinal follow-up and performance validation of an mRNA-based urine test (Xpert((R)) Bladder Cancer Monitor ) for surveillance in patients with non-muscle-invasive bladder cancer. BJU Int. Dec 2021;128(6):713-721. doi:10.1111/bju.15418.
204. Cronin M, Pho M, Dutta D, et al. Measurement of Gene Expression in Archival Paraffin-Embedded Tissues. American Journal of Pathology. 2004;164(35-42).
205. Cronin M, Sangli C, Liu ML, et al. Analytical validation of the Oncotype DX genomic diagnostic test for recurrence prognosis and therapeutic response prediction in node-negative, estrogen receptor-positive breast cancer. Clin Chem. Jun 2007;53(6):1084-91. doi:10.1373/clinchem.2006.076497.
206. Cullen J, Kuo HC, Shan J, Lu R, Aboushwareb T, Van Den Eeden SK. The 17-Gene Genomic Prostate Score Test as a Predictor of Outcomes in Men with Unfavorable Intermediate Risk Prostate Cancer. Urology. Sep 2020;143:103-111. doi:10.1016/j.urology.2020.05.045.
207. Cullen J, Lynch JA, Klein EA, et al. Multicenter Comparison of 17-Gene Genomic Prostate Score as a Predictor of Outcomes in African American and Caucasian American Men with Clinically Localized Prostate Cancer. J Urol. Apr 2021;205(4):1047-1054. doi:10.1097/JU.0000000000001484.
208. Cullen J, Rosner IL, Brand TC, et al. A Biopsy-based 17-gene Genomic Prostate Score Predicts Recurrence After Radical Prostatectomy and Adverse Surgical Pathology in a Racially Diverse Population of Men with Clinically Low- and Intermediate-risk Prostate Cancer. Eur Urol. Jul 2015;68(1):123-31. doi:10.1016/j.eururo.2014.11.030.
209. Cuzick J, Berney DM, Fisher G, et al. Prognostic value of a cell cycle progression signature for prostate cancer death in a conservatively managed needle biopsy cohort. Br J Cancer. Mar 13 2012;106(6):1095-9. doi:10.1038/bjc.2012.39.
210. Cuzick J, Stone S, Fisher G, et al. Validation of an RNA cell cycle progression score for predicting death from prostate cancer in a conservatively managed needle biopsy cohort. Br J Cancer. Jul 28 2015;113(3):382-9. doi:10.1038/bjc.2015.223.
211. Dall'Era MA, Maddala T, Polychronopoulos L, Gallagher JR, Febbo PG, Denes BS. Utility of the Oncotype DX((R)) Prostate Cancer Assay in Clinical Practice for Treatment Selection in Men Newly Diagnosed with Prostate Cancer: A Retrospective Chart Review Analysis. Urol Pract. Nov 2015;2(6):343-348. doi:10.1016/j.urpr.2015.02.007.
212. Daneshmand S, Patel S, Lotan Y, et al. Efficacy and Safety of Blue Light Flexible Cystoscopy with Hexaminolevulinate in the Surveillance of Bladder Cancer: A Phase III, Comparative, Multicenter Study. J Urol. May 2018;199(5):1158-1165. doi:10.1016/j.juro.2017.11.096.
213. Daver N, Venugopal S, Ravandi F. FLT3 mutated acute myeloid leukemia: 2021 treatment algorithm. Blood Cancer J. May 27 2021;11(5):104. doi:10.1038/s41408-021-00495-3.
214. Davey MG, Cleere EF, O'Donnell JP, Gaisor S, Lowery AJ, Kerin MJ. Value of the 21-gene expression assay in predicting locoregional recurrence rates in estrogen receptor-positive breast cancer: a systematic review and network meta-analysis. Breast Cancer Res Treat. Jun 2022;193(3):535-544. doi:10.1007/s10549-022-06580-w.
215. Davey MG, Jalali A, Ryan EJ, et al. A Novel Surrogate Nomogram Capable of Predicting OncotypeDX Recurrence Score(c). J Pers Med. Jul 8 2022;12(7)doi:10.3390/jpm12071117.
216. Davey MG, Ryan EJ, Abd Elwahab S, et al. Clinicopathological correlates, oncological impact, and validation of Oncotype DX in a European Tertiary Referral Centre. Breast J. Jun 2021;27(6):521-528. doi:10.1111/tbj.14217.
217. de la Calle C, Patil D, Wei JT, et al. Multicenter Evaluation of the Prostate Health Index to Detect Aggressive Prostate Cancer in Biopsy Naive Men. J Urol. Jul 2015;194(1):65-72. doi:10.1016/j.juro.2015.01.091.
218. Del Chiaro M, Verbeke C, Salvia R, et al. European experts consensus statement on cystic tumours of the pancreas. Dig Liver Dis. Sep 2013;45(9):703-11. doi:10.1016/j.dld.2013.01.010.
219. Department of Health and Human Services. Medicare Program; Hospital Inpatient Prospective Payment Systems for Acute Care Hospitals and the LongTerm Care Hospital Prospective Payment System and Policy Changes and Fiscal Year 2022 Rates; Quality Programs and Medicare Promoting Interoperability Program Requirements for Eligible Hospitals and Critical Access Hospitals; Changes to Medicaid Provider Enrollment; and Changes to the Medicare Shared Savings Program. In: Services DoHaH, editor. 2021.
220. Diehl F, Schmidt K, Choti MA, et al. Circulating mutant DNA to assess tumor dynamics. Nat Med. Sep 2008;14(9):985-90. doi:10.1038/nm.1789.
221. Dighe S, Purkayastha S, Swift I, et al. Diagnostic precision of CT in local staging of colon cancers: a meta-analysis. Clin Radiol. Sep 2010;65(9):708-19. doi:10.1016/j.crad.2010.01.024.
222. Dimashkieh H, Wolff DJ, Smith TM, Houser PM, Nietert PJ, Yang J. Evaluation of urovysion and cytology for bladder cancer detection: a study of 1835 paired urine samples with clinical and histologic correlation. Cancer Cytopathol. Oct 2013;121(10):591-7. doi:10.1002/cncy.21327.
223. Ding T, Wang YK, Cao YH, Yang LY. Clinical utility of fluorescence in situ hybridization for prediction of residual tumor after transurethral resection of bladder urothelial carcinoma. Urology. Apr 2011;77(4):855-9. doi:10.1016/j.urology.2010.09.061.
224. Dinneen E, Shaw GL, Kealy R, et al. Feasibility of aspirin and/or vitamin D3 for men with prostate cancer on active surveillance with Prolaris(R) testing. BJUI Compass. Nov 2022;3(6):458-465. doi:10.1002/bco2.169.
225. Duick DS, Klopper JP, Diggans JC, et al. The impact of benign gene expression classifier test results on the endocrinologist-patient decision to operate on patients with thyroid nodules with indeterminate fine-needle aspiration cytopathology. Thyroid. Oct 2012;22(10):996-1001. doi:10.1089/thy.2012.0180.
226. Ebell MH, Siwek J, Weiss BD, et al. Strength of Recommendation Taxonomy (SORT): A Patient-Centered Approach to Grading Evidence in the Medical Literature. American Family Physician. 2004;69(3):548-556.
227. Edmondson AJ, Birtwistle JC, Catto JWF, Twiddy M. The patients' experience of a bladder cancer diagnosis: a systematic review of the qualitative evidence. J Cancer Surviv. Aug 2017;11(4):453-461. doi:10.1007/s11764-017-0603-6.
228. Eggener SE, Rumble RB, Armstrong AJ, et al. Molecular Biomarkers in Localized Prostate Cancer: ASCO Guideline. J Clin Oncol. May 1 2020;38(13):1474-1494. doi:10.1200/JCO.19.02768.
229. Engstrand J, Nilsson H, Stromberg C, Jonas E, Freedman J. Colorectal cancer liver metastases - a population-based study on incidence, management and survival. BMC Cancer. Jan 15 2018;18(1):78. doi:10.1186/s12885-017-3925-x.
230. Engstrom PF, Bloom MG, Demetri GD, et al. NCCN molecular testing white paper: effectiveness, efficiency, and reimbursement. J Natl Compr Canc Netw. Dec 2011;9 Suppl 6:S1-16. doi:10.6004/jnccn.2011.0138.
231. Fang P, He W, Gomez D, et al. Racial disparities in guideline-concordant cancer care and mortality in the United States. Adv Radiat Oncol. Jul-Sep 2018;3(3):221-229. doi:10.1016/j.adro.2018.04.013.
232. Fantony JJ, Inman BA. It May Be Time to Abandon Urine Tests for Bladder Cancer. J Natl Compr Canc Netw. Sep 2015;13(9):1163-6. doi:10.6004/jnccn.2015.0141.
233. Ferguson JS, Van Wert R, Choi Y, et al. Impact of a bronchial genomic classifier on clinical decision making in patients undergoing diagnostic evaluation for lung cancer. BMC Pulm Med. May 17 2016;16(1):66. doi:10.1186/s12890-016-0217-1.
234. Finkelstein SD, Sistrunk JW, Malchoff C, et al. A Retrospective Evaluation of the Diagnostic Performance of an Interdependent Pairwise MicroRNA Expression Analysis with a Mutation Panel in Indeterminate Thyroid Nodules. Thyroid. Nov 2022;32(11):1362-1371. doi:10.1089/thy.2022.0124.
235. Freedland SJ, Gerber L, Reid J, et al. Prognostic utility of cell cycle progression score in men with prostate cancer after primary external beam radiation therapy. Int J Radiat Oncol Biol Phys. Aug 1 2013;86(5):848-53. doi:10.1016/j.ijrobp.2013.04.043.
236. Fryback DG, Thornbury JR. The Efficacy of Diagnostic Imaging. Medical Decision Making. 1991;11(2):88-94. doi:10.1177/0272989X9101100203.
237. Gage MM, Hueman MT. Colorectal Cancer Surveillance: What Is the Optimal Frequency of Follow-up and Which Tools Best Predict Recurrence? Current Colorectal Cancer Reports. 2017;13(4):316-324. doi:10.1007/s11888-017-0382-5.
238. Garcia-Pardo M, Czarnecka-Kujawa K, Law JH, et al. Association of Circulating Tumor DNA Testing Before Tissue Diagnosis With Time to Treatment Among Patients With Suspected Advanced Lung Cancer: The ACCELERATE Nonrandomized Clinical Trial. JAMA Netw Open. Jul 3 2023;6(7):e2325332. doi:10.1001/jamanetworkopen.2023.25332.
239. Garrido MM, Bernardino RM, Marta JC, Holdenrieder S, Guimaraes JT. Tumour markers in prostate cancer: The post-prostate-specific antigen era. Ann Clin Biochem. Jan 2022;59(1):46-58. doi:10.1177/00045632211041890.
240. Garrigou S, Perkins G, Garlan F, et al. A Study of Hypermethylated Circulating Tumor DNA as a Universal Colorectal Cancer Biomarker. Clin Chem. Aug 2016;62(8):1129-39. doi:10.1373/clinchem.2015.253609.
241. Genentech Healthcare Systems Cost Savings and Coverage of NGS. https://www.biomarkertesting.com/#ngs-coverage.
242. Gibson FT, Murad F, Granger E, Schmults CD, Ruiz ES. Perioperative imaging for high-stage cutaneous squamous cell carcinoma helps guide management in nearly a third of cases: A single-institution retrospective cohort. J Am Acad Dermatol. May 2023;88(5):1209-1211. doi:10.1016/j.jaad.2023.01.012.
243. Goodman A. More Widespread Biomarker Testing for NSCLC in Oncology Practices and More Testing in Black Patients: An Urgent Priority. Conexiant Oncology. https://ascopost.com/issues/august-25-2021/more-widespread-biomarker-testing-for-nsclc-in-oncology-practices-and-more-testing-in-black-patients/.
244. Gopalakrishna A, Fantony JJ, Longo TA, et al. Anticipatory Positive Urine Tests for Bladder Cancer. Ann Surg Oncol. Jun 2017;24(6):1747-1753. doi:10.1245/s10434-016-5763-5.
245. Gopalakrishna A, Longo TA, Fantony JJ, et al. The diagnostic accuracy of urine-based tests for bladder cancer varies greatly by patient. BMC Urol. Jun 13 2016;16(1):30. doi:10.1186/s12894-016-0147-5.
246. Gore SM, Shaw D, Martin RC, et al. Prospective study of sentinel node biopsy for high-risk cutaneous squamous cell carcinoma of the head and neck. Head Neck. Apr 2016;38 Suppl 1:E884-9. doi:10.1002/hed.24120.
247. Gosney JR, Paz-Ares L, Janne P, et al. Pathologist-initiated reflex testing for biomarkers in non-small-cell lung cancer: expert consensus on the rationale and considerations for implementation. ESMO Open. Aug 2023;8(4):101587. doi:10.1016/j.esmoop.2023.101587.
248. Grobmyer SR, Cance WG, Copeland EM, Vogel SB, Hochwald SN. Is there an indication for initial conservative management of pancreatic cystic lesions? J Surg Oncol. Oct 1 2009;100(5):372-4. doi:10.1002/jso.21260.
249. Haese A, Trooskens G, Steyaert S, et al. Multicenter Optimization and Validation of a 2-Gene mRNA Urine Test for Detection of Clinically Significant Prostate Cancer before Initial Prostate Biopsy. J Urol. Aug 2019;202(2):256-263. doi:10.1097/JU.0000000000000293.
250. Hajdinjak T. UroVysion FISH test for detecting urothelial cancers: meta-analysis of diagnostic accuracy and comparison with urinary cytology testing. Urol Oncol. Nov-Dec 2008;26(6):646-51. doi:10.1016/j.urolonc.2007.06.002.
251. Halling KC, King W, Sokolova IA, et al. Comparison of Cytology and Fluorescence in Situ Hybridization for the Detection of Urothelial Carcinoma. The Journal of Urology. 2000;164:1768-1775.
252. Halpern N, Sonnenblick A, Uziely B, et al. Oncotype Dx recurrence score among BRCA1/2 germline mutation carriers with hormone receptors positive breast cancer. Int J Cancer. May 1 2017;140(9):2145-2149. doi:10.1002/ijc.30616.
253. Harris NL, Jaffe ES, Diebold J, et al. The World Health Organization classification of hematological malignancies report of the Clinical Advisory Committee Meeting, Airlie House, Virginia, November 1997. Mod Pathol. Feb 2000;13(2):193-207. doi:10.1038/modpathol.3880035.
254. Haslem DS, Van Norman SB, Fulde G, et al. A Retrospective Analysis of Precision Medicine Outcomes in Patients With Advanced Cancer Reveals Improved Progression-Free Survival Without Increased Health Care Costs. J Oncol Pract. Feb 2017;13(2):e108-e119. doi:10.1200/JOP.2016.011486.
255. Hayashi Y, Fujita K, Netto GJ, Nonomura N. Clinical Application of TERT Promoter Mutations in Urothelial Carcinoma. Front Oncol. 2021;11:705440. doi:10.3389/fonc.2021.705440.
256. He H, Han C, Hao L, Zang G. ImmunoCyt test compared to cytology in the diagnosis of bladder cancer: A meta-analysis. Oncol Lett. Jul 2016;12(1):83-88. doi:10.3892/ol.2016.4556.
257. He J, Abdel-Wahab O, Nahas MK, et al. Integrated genomic DNA/RNA profiling of hematologic malignancies in the clinical setting. Blood. Jun 16 2016;127(24):3004-14. doi:10.1182/blood-2015-08-664649.
258. Health and Human Services Office of Inspector General. CMS’s Oversight of Medicare Payments for the Highest Paid Molecular Pathology Genetic Test Was Not Adequate To Reduce the Risk of up to $888 Million in Improper Payments. OIG Report. General OoI; 2023. A-09-22-03010. June 2023.
259. Healthcare Fraud Prevention Partnership. Genetic Testing: Fraud, Waste, & Abuse White Paper. In: Centers for Medicare and Medicaid Services, editor. 2020.
260. Henderson J, Adams P, Barber K. Factors Determining Anthracycline Use in Hormone Receptor Positive, Early-Stage Breast Cancer. Clin Breast Cancer. Jun 2019;19(3):e475-e480. doi:10.1016/j.clbc.2019.01.012.
261. Hescot S, Al Ghuzlan A, Henry T, et al. Prognostic of recurrence and survival in poorly differentiated thyroid cancer. Endocr Relat Cancer. Nov 1 2022;29(11):625-634. doi:10.1530/ERC-22-0151.
262. Hirsch P, Tang R, Abermil N, et al. Precision and prognostic value of clone-specific minimal residual disease in acute myeloid leukemia. Haematologica. Jul 2017;102(7):1227-1237. doi:10.3324/haematol.2016.159681.
263. Hu M, Kim ANH, Emeto TI, Collins M, Chopping A, Lin C. Metastatic cutaneous squamous cell carcinoma to the parotid: Adjuvant radiotherapy and treatment outcomes. J Med Radiat Sci. Jun 2023;70(2):161-170. doi:10.1002/jmrs.650.
264. Hu X, Li G, Wu S. Advances in Diagnosis and Therapy for Bladder Cancer. Cancers (Basel). Jun 29 2022;14(13)doi:10.3390/cancers14133181.
265. Hughes BGM, Munoz-Couselo E, Mortier L, et al. Pembrolizumab for locally advanced and recurrent/metastatic cutaneous squamous cell carcinoma (KEYNOTE-629 study): an open-label, nonrandomized, multicenter, phase II trial. Ann Oncol. Oct 2021;32(10):1276-1285. doi:10.1016/j.annonc.2021.07.008.
266. Issa AM, Chaudhari VS, Marchant GE. The value of multigene predictors of clinical outcome in breast cancer: an analysis of the evidence. Expert Rev Mol Diagn. Feb 2015;15(2):277-86. doi:10.1586/14737159.2015.983476.
267. Jaafar H, Bashir MA, Taher A, Qawasmeh K, Jaloudi M. Impact of Oncotype DX testing on adjuvant treatment decisions in patients with early breast cancer: a single-center study in the United Arab Emirates. Asia Pac J Clin Oncol. Dec 2014;10(4):354-60. doi:10.1111/ajco.12259.
268. Janes JL, Boyer MJ, Bennett JP, et al. The 17-Gene Genomic Prostate Score Test Is Prognostic for Outcomes After Primary External Beam Radiation Therapy in Men With Clinically Localized Prostate Cancer. Int J Radiat Oncol Biol Phys. Jan 1 2023;115(1):120-131. doi:10.1016/j.ijrobp.2022.06.101.
269. Jayasekera J, Sparano JA, Gray R, et al. Simulation Modeling to Extend Clinical Trials of Adjuvant Chemotherapy Guided by a 21-Gene Expression Assay in Early Breast Cancer. JNCI Cancer Spectr. Dec 2019;3(4):pkz062. doi:10.1093/jncics/pkz062.
270. Jedi M, Young GP, Pedersen SK, Symonds EL. Methylation and Gene Expression of BCAT1 and IKZF1 in Colorectal Cancer Tissues. Clin Med Insights Oncol. 2018;12:1179554918775064. doi:10.1177/1179554918775064.
271. Jongen-Lavrencic M, Grob T, Hanekamp D, et al. Molecular Minimal Residual Disease in Acute Myeloid Leukemia. N Engl J Med. Mar 29 2018;378(13):1189-1199. doi:10.1056/NEJMoa1716863.
272. Kalinsky K, Barlow WE, Gralow JR, et al. 21-Gene Assay to Inform Chemotherapy Benefit in Node-Positive Breast Cancer. N Engl J Med. Dec 16 2021;385(25):2336-2347. doi:10.1056/NEJMoa2108873.
273. Kamat AM, Dickstein RJ, Messetti F, et al. Use of fluorescence in situ hybridization to predict response to bacillus Calmette-Guerin therapy for bladder cancer: results of a prospective trial. J Urol. Mar 2012;187(3):862-7. doi:10.1016/j.juro.2011.10.144.
274. Kamat AM, Karam JA, Grossman HB, Kader AK, Munsell M, Dinney CP. Prospective trial to identify optimal bladder cancer surveillance protocol: reducing costs while maximizing sensitivity. BJU Int. Oct 2011;108(7):1119-23. doi:10.1111/j.1464-410X.2010.10026.x.
275. Kamat AM, Willis DL, Dickstein RJ, et al. Novel fluorescence in situ hybridization-based definition of bacille Calmette-Guerin (BCG) failure for use in enhancing recruitment into clinical trials of intravesical therapies. BJU Int. May 2016;117(5):754-60. doi:10.1111/bju.13186.
276. Kantor O, Barrera E, Kopkash K, et al. Are we Overtreating Hormone Receptor Positive Breast Cancer with Neoadjuvant Chemotherapy? Role of OncotypeDx((R)) for Hormone Receptor Positive Patients Undergoing Neoadjuvant Chemotherapy. Ann Surg Oncol. Oct 2019;26(10):3232-3239. doi:10.1245/s10434-019-07555-w.
277. Kehinde EO, Al-Mulla F, Kapila K, Anim JT. Comparison of the sensitivity and specificity of urine cytology, urinary nuclear matrix protein-22 and multitarget fluorescence in situ hybridization assay in the detection of bladder cancer. Scand J Urol Mar 2011;45(2):113-21. doi:10.3109/00365599.2010.533694.
278. Kico JM, Miller CA, Griffith M, et al. Association Between Mutation Clearance After Induction Therapy and Outcomes in Acute Myeloid Leukemia. JAMA. Aug 25 2015;314(8):811-22. doi:10.1001/jama.2015.9643.
279. Kim K, Jung J, Shin KH, et al. Impact of Oncotype DX Recurrence Score on the Patterns of Locoregional Recurrence in Breast Cancer (Korean Radiation Oncology Group 19-06). J Breast Cancer. Jun 2020;23(3):314-319. doi:10.4048/jbc.2020.23.e36.
280. Kim SP, Meropol NJ, Gross CP, et al. Physician attitudes about genetic testing for localized prostate cancer: A national survey of radiation oncologists and urologists. Urol Oncol. 2018;36(11):501e15-501e21. doi:10.1016/j.urolonc.2018.07.002.
281. Kiyoi H, Kawashima N, Ishikawa Y. FLT3 mutations in acute myeloid leukemia: Therapeutic paradigm beyond inhibitor development. Cancer Sci. Feb 2020;111(2):312-322. doi:10.1111/cas.14274.
282. Klein EA, Cooperberg MR, Magi-Galluzzi C, et al. A 17-gene Assay to Predict Prostate Cancer Aggressiveness in the Context of Gleason Grade Heterogeneity, Tumor Multifocality, and Biopsy Undersampling. Eur Urol. 2014;66(3):550-560. doi:10.1016/j.eururo.2014.05.004.
283. Knezevic D, Goddard AD, Natraj N, et al. Analytical validation of the Oncotype DX prostate cancer assay –a clinical RT-PCR assay optimized for prostate needle biopsies. BMC Genomics. 2013;14(1):690. doi:10.1186/1471-2164-14-690.
284. Koh M, Jung J, Kim SS, et al. Prognostic value of the 21-gene recurrence score for regional recurrence in patients with estrogen receptor-positive breast cancer. Breast Cancer Res Treat. Aug 2021;188(3):583-592. doi:10.1007/s10549-021-06228-1.
285. Konishi T, Shimada Y, Hsu M, et al. Association of Preoperative and Postoperative Serum Carcinoembryonic Antigen and Colon Cancer Outcome. JAMA Oncol. Mar 1 2018;4(3):309-315. doi:10.1001/jamaoncol.2017.4420.
286. Koopmann BDM, Dunnewind N, van Duuren LA, et al. The Natural Disease Course of Pancreatic Cyst-Associated Neoplasia, Dysplasia, and Ductal Adenocarcinoma: Results of a Microsimulation Model. Gastroenterology. Dec 2023;165(6):1522-1532. doi:10.1053/j.gastro.2023.08.027.
287. Kotzer KE, Riley JD, Conta JH, Anderson CM, Schahl KA, Goodenberger ML. Genetic testing utilization and the role of the laboratory genetic counselor. Clin Chim Acta. 2014;427:193-195. doi:10.1016/j.cca.2013.09.033.
288. Kuhl V, Clegg W, Meek S. Development and validation of a cell cycle progression signature for decentralized testing of men with prostate cancer. Biomark Med. 2022;16(6):449-459. doi:doi:10.2217/bmm-2021-0479.
289. Landaas EJ, Eckel AM, Wright JL, Baird GS, Hansen RN, Sullivan SD. Application of Health Technology Assessment (HTA) to Evaluate New Laboratory Tests in a Health System: A Case Study of Bladder Cancer Testing. Acad Pathol. Jan-Dec 2020;7:2374289520968225. doi:10.1177/2374289520968225.
290. Laviana AA, Chang SS. Guidelines for muscle invasive bladder cancer. American Urological Association; 2018.
291. Lee SH, Ha S, An HJ, et al. Association between partial-volume corrected SUVmax and Oncotype DX recurrence score in early-stage, ER-positive/HER2-negative invasive breast cancer. Eur J Nucl Med Mol Imaging. Aug 2016;43(9):1574-84. doi:10.1007/s00259-016-3418-1.
292. Lehto TK, Sturenberg C, Malen A, et al. Transcript analysis of commercial prostate cancer risk stratification panels in hard-to-predict grade group 2-4 prostate cancers. Prostate. May 2021;81(7):368-376. doi:10.1002/pros.24108.
293. Lewin R, Sulkes A, Shochat T, et al. Oncotype-DX recurrence score distribution in breast cancer patients with BRCA1/2 mutations. Breast Cancer Res Treat. Jun 2016;157(3):511-6. doi:10.1007/s10549-016-3836-6.
294. Li GQ, Xie SJ, Wu SG, He ZY. Impact of the 21-gene expression assay on treatment decisions and clinical outcomes in breast cancer with one to three positive lymph nodes. Front Endocrinol (Lausanne). 2023;14:1103949. doi:10.3389/fendo.2023.1103949.
295. Likhacheva A, Awan M, Barker CA, et al. Definitive and Postoperative Radiation Therapy for Basal and Squamous Cell Cancers of the Skin: Executive Summary of an American Society for Radiation Oncology Clinical Practice Guideline. Pract Radiat Oncol. Jan-Feb 2020;10(1):8-20. doi:10.1016/j.prro.2019.10.014.
296. Lin DW, Zheng Y, McKenney JK, et al. 17-Gene Genomic Prostate Score Test Results in the Canary Prostate Active Surveillance Study (PASS) Cohort. J Clin Oncol. May 10 2020;38(14):1549-1557. doi:10.1200/JCO.19.02267.
297. Litvak A, Cercek A, Segal N, et al. False-positive elevations of carcinoembryonic antigen in patients with a history of resected colorectal cancer. J Natl Compr Canc Netw. Jun 2014;12(6):907-13. doi:10.6004/jnccn.2014.0085.
298. Liu J, Suresh A, Palettas M, et al. Features, Outcomes, and Management Strategies of Male Breast Cancer: A Single Institution Comparison to Well-Matched Female Controls. Eur J Breast Health. Jul 2020;16(3):201-207. doi:10.5152/ejbh.2020.5536.
299. Locke WJ, Guanzon D, Ma C, et al. DNA Methylation Cancer Biomarkers: Translation to the Clinic. Front Genet. 2019;10:1150. doi:10.3389/fgene.2019.01150.
300. Lohse I, Azzam DJ, Al-Ali H, et al. Ovarian Cancer Treatment Stratification Using Ex Vivo Drug Sensitivity Testing. Anticancer Res. Aug 2019;39(8):4023-4030. doi:10.21873/anticanres.13558.
301. Lokeshwar SD, Syed JS, Segal D, Rahman SN, Sprenkle PC. Optimal Use of Tumor-Based Molecular Assays for Localized Prostate Cancer. Curr Oncol Rep. Feb 2022;24(2):249-256. doi:10.1007/s11912-021-01180-1.
302. Lopez M, Parreco J, Buicko J, Rishi R, Billingsley K, Castillo A. The true cost of high volume whipple procedures. Hpb. 2018;20:S45-S46. doi:10.1016/j.hpb.2018.02.248.
303. Lopez-Beltran A, Cheng L, Gevaert T, et al. Current and emerging bladder cancer biomarkers with an emphasis on urine biomarkers. Expert Rev Mol Diagn. Feb 2020;20(2):231-243. doi:10.1080/14737159.2020.1699791.
304. Lotan Y, Bensalah K, Ruddell T, Shariat SF, Sagalowsky AI, Ashfaq R. Prospective evaluation of the clinical usefulness of reflex fluorescence in situ hybridization assay in patients with atypical cytology for the detection of urothelial carcinoma of the bladder. J Urol. Jun 2008;179(6):2164-9. doi:10.1016/j.juro.2008.01.105.
305. Lotan Y, Inman BA, Davis LG, et al. Evaluation of the Fluorescence In Situ Hybridization Test to Predict Recurrence and/or Progression of Disease after bacillus Calmette-Guerin for Primary High Grade Nonmuscle Invasive Bladder Cancer: Results from a Prospective Multicenter Trial. J Urol. Nov 2019;202(5):920-926. doi:10.1097/JU.0000000000000355.
306. Lozano F, Raventos CX, Carrion A, Trilla E, Morote J. Current status of genetic urinary biomarkers for surveillance of non-muscle invasive bladder cancer: a systematic review. BMC Urol. Jul 14 2020;20(1):99. doi:10.1186/s12894-020-00670-x.
307. Lynch TJ, Bell DW, Sordella R, et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med. May 20 2004;350(21):2129-39. doi:10.1056/NEJMoa040938.
308. Magi-Galluzzi C, Isharwal S, Falzarano SM, et al. The 17-Gene Genomic Prostate Score Assay Predicts Outcome After Radical Prostatectomy Independent of PTEN Status. Urology. Nov 2018;121:132-138. doi:10.1016/j.urology.2018.07.018.
309. Magi-Galluzzi C, Maddala T, Falzarano SM, et al. Gene expression in normal-appearing tissue adjacent to prostate cancers are predictive of clinical outcome: evidence for a biologically meaningful field effect. Oncotarget. 2016;7(23):33855-33865. doi:10.18632/oncotarget.8944.
310. Mahajan S, Barker CA, Mauguen A, Singh B, Pandit-Taskar N. Restaging [(18)F] fludeoxyglucose positron emission tomography/computed tomography scan in recurrent cutaneous squamous cell carcinoma: Diagnostic performance and prognostic significance. J Am Acad Dermatol. Apr 2020;82(4):878-886. doi:10.1016/j.jaad.2019.09.035.
311. Malani D, Kumar A, Bruck O, et al. Implementing a Functional Precision Medicine Tumor Board for Acute Myeloid Leukemia. Cancer Discov. Feb 2022;12(2):388-401. doi:10.1158/2159-8290.CD-21-0410.
312. Malla M, Loree JM, Kasi PM, Parikh AR. Using Circulating Tumor DNA in Colorectal Cancer: Current and Evolving Practices. J Clin Oncol. Aug 20 2022;40(24):2846-2857. doi:10.1200/JCO.21.02615.
313. Malmberg EB, Stahlman S, Rehammar A, et al. Patient-tailored analysis of minimal residual disease in acute myeloid leukemia using next-generation sequencing. Eur J Haematol. Jan 2017;98(1):26-37. doi:10.1111/ejh.12780.
314. Mamounas EP, Liu Q, Paik S, et al. 21-Gene Recurrence Score and Locoregional Recurrence in Node-Positive/ER-Positive Breast Cancer Treated With Chemo-Endocrine Therapy. J Natl Cancer Inst. Jan 2017;109(4)doi:10.1093/jnci/djw259.
315. Mamounas EP, Tang G, Fisher B, et al. Association between the 21-gene recurrence score assay and risk of locoregional recurrence in node-negative, estrogen receptor-positive breast cancer: results from NSABP B-14 and NSABP B-20. J Clin Oncol. Apr 1 2010;28(10):1677-83. doi:10.1200/JCO.2009.23.7610.
316. Markowitz SD, Bertagnolli MM. Molecular origins of cancer: Molecular basis of colorectal cancer. N Engl J Med. Dec 17 2009;361(25):2449-60. doi:10.1056/NEJMra0804588.
317. Marrazzo G, Zitelli JA, Brodland D. Clinical outcomes in high-risk squamous cell carcinoma patients treated with Mohs micrographic surgery alone. J Am Acad Dermatol. Mar 2019;80(3):633-638. doi:10.1016/j.jaad.2018.09.015.
318. Martin NA, Tepper JE, Giri VN, et al. Adopting Consensus Terms for Testing in Precision Medicine. JCO Precis Oncol. 2021;5doi:10.1200/PO.21.00027.
319. Masarwy R, Shilo S, Carmel Neiderman NN, et al. The Prognostic Value and Clinical Utility of the 40-Gene Expression Profile (40-GEP) Test in Cutaneous Squamous Cell Carcinoma: Systematic Review and Meta-Analysis. Cancers (Basel). Apr 25 2023;15(9)doi:10.3390/cancers15092456.
320. McCartney A, Vignoli A, Tenori L, et al. Metabolomic analysis of serum may refine 21-gene expression assay risk recurrence stratification. NPJ Breast Cancer. 2019;5:26. doi:10.1038/s41523-019-0123-9.
321. McKiernan J, Donovan MJ, O'Neill V, et al. A Novel Urine Exosome Gene Expression Assay to Predict High-grade Prostate Cancer at Initial Biopsy. JAMA Oncol. Jul 1 2016;2(7):882-9. doi:10.1001/jamaoncol.2016.0097.
322. Memorial Sloan Kettering Cancer Center. OncoKB. https://www.oncokb.org/.
323. Mengual L, Marin-Aguilera M, Ribal MJ, et al. Clinical utility of fluorescent in situ hybridization for the surveillance of bladder cancer patients treated with bacillus Calmette-Guerin therapy. Eur Urol. Sep 2007;52(3):752-9. doi:10.1016/j.eururo.2007.03.001.
324. Meyerhardt JA, Mangu PB, Flynn PJ, et al. Follow-up care, surveillance protocol, and secondary prevention measures for survivors of colorectal cancer: American Society of Clinical Oncology clinical practice guideline endorsement. J Clin Oncol. Dec 10 2013;31(35):4465-70. doi:10.1200/JCO.2013.50.7442.
325. Migden MR, Rischin D, Sasane M, et al. Health-Related Quality of Life (HRQL) in Patients with Advanced Cutaneous Squamous Cell Carcinoma (CSCC) Treated with Cemiplimab: Post Hoc Exploratory Analysis of a Phase 2 Clinical Trial. 2020.
326. Migden MR, Rischin D, Schmults CD, et al. PD-1 Blockade with Cemiplimab in Advanced Cutaneous Squamous-Cell Carcinoma. N Engl J Med. Jul 26 2018;379(4):341-351. doi:10.1056/NEJMoa1805131.
327. Miller CE, Krautscheid P, Baldwin EE, et al. Genetic counselor review of genetic test orders in a reference laboratory reduces unnecessary testing. Am J Med Genet A. May 2014;164A(5):1094-101. doi:10.1002/ajmg.a.36453.
328. Mitchell SM, Ho T, Brown GS, et al. Evaluation of Methylation Biomarkers for Detection of Circulating Tumor DNA and Application to Colorectal Cancer. Genes (Basel). Dec 15 2016;7(12)doi:10.3390/genes7120125.
329. Mitchell SM, Ross JP, Drew HR, et al. A panel of genes methylated with high frequency in colorectal cancer. BMC Cancer. Jan 31 2014;14:54. doi:10.1186/1471-2407-14-54.
330. Moertel CG, Fleming TR, Macdonald JS, Haller DG, Laurie JA, Tangen C. An evaluation of the carcinoembryonic antigen (CEA) test for monitoring patients with resected colon cancer. Jama. Aug 25 1993;270(8):943-7.
331. Monaghan PJ, Robinson S, Rajdl D, et al. Practical guide for identifying unmet clinical needs for biomarkers. EJIFCC. 2018;29(2):129-137.
332. Morgan RO, Teal CR, Hasche JC, et al. Does poorer familiarity with Medicare translate into worse access to health care? J Am Geriatr Soc. Nov 2008;56(11):2053-60. doi:10.1111/j.1532-5415.2008.01993.x.
333. Morin RD, Arthur SE, Hodson DJ. Molecular profiling in diffuse large B-cell lymphoma: why so many types of subtypes? Br J Haematol. Feb 2022;196(4):814-829. doi:10.1111/bjh.17811.
334. Morita K, Kantarjian HM, Wang F, et al. Clearance of Somatic Mutations at Remission and the Risk of Relapse in Acute Myeloid Leukemia. J Clin Oncol. Jun 20 2018;36(18):1788-1797. doi:10.1200/JCO.2017.77.6757.
335. Morris V, Dasari A, Kopetz S. Can Circulating Tumor DNA in Early-Stage Colorectal Cancer Be More Than a Prognostic Biomarker? JAMA Oncol. Aug 1 2019;5(8):1101-1103. doi:10.1001/jamaoncol.2019.0503.
336. Murphy AB, Carbunaru S, Nettey OS, et al. A 17-Gene Panel Genomic Prostate Score Has Similar Predictive Accuracy for Adverse Pathology at Radical Prostatectomy in African American and European American Men. Urology. Aug 2020;142:166-173. doi:10.1016/j.urology.2020.01.052.
337. Mutonga M, Speedy S, Rademaker A, et al. Relationship of pathological features and a 21 gene expression assay in younger versus older women with node-negative endocrine receptor-positive breast cancer. Breast Cancer Res Treat. Jul 2019;176(1):95-100. doi:10.1007/s10549-018-05088-6.
338. Muzic JG, Schmitt AR, Wright AC, et al. Incidence and Trends of Basal Cell Carcinoma and Cutaneous Squamous Cell Carcinoma: A Population-Based Study in Olmsted County, Minnesota, 2000 to 2010. Mayo Clin Proc. Jun 2017;92(6):890-898. doi:10.1016/j.mayocp.2017.02.015.
339. National Comprehensive Cancer Network. Breast Cancer. Accessed December 19, 2024. https://www.nccn.org/guidelines/guidelines-detail?category=1&id=1419.
340. National Comprehensive Cancer Network. Colon Cancer. Accessed December 19, 2024. https://www.nccn.org/guidelines/guidelines-detail?category=1&id=1428.
341. National Comprehensive Cancer Network. Kidney Cancer. Accessed December 19, 2024. https://www.nccn.org/guidelines/guidelines-detail?category=1&id=1440.
342. National Comprehensive Cancer Network. Non-Small Cell Lung Cancer. Accessed December 19, 2024. https://www.nccn.org/guidelines/guidelines-detail?category=1&id=1450.
343. National Comprehensive Cancer Network. Prostate Cancer. Accessed December 19, 2024. https://www.nccn.org/guidelines/guidelines-detail?category=1&id=1459.
344. National Comprehensive Cancer Network. Rectal Cancer. Accessed December 19, 2024. https://www.nccn.org/guidelines/guidelines-detail?category=1&id=1461.
345. Newcomer LN, Weininger R, Carlson RW. Transforming Prior Authorization to Decision Support. J Oncol Pract. Jan 2017;13(1):e57-e61. doi:10.1200/JOP.2016.015198.
346. Nicholson A, Mahon J, Boland A, et al. The clinical effectiveness and cost-effectiveness of the PROGENSA® prostate cancer antigen 3 assay and the Prostate Health Index in the diagnosis of prostate cancer: a systematic review and economic evaluation. Health Techonology Assessment. 2015;19(87):1-191. doi:10.3310/hta19870.
347. Nicholson BD, Shinkins B, Pathiraja I, et al. Blood CEA levels for detecting recurrent colorectal cancer. Cochrane Database Syst Rev. Dec 10 2015;2015(12):CD011134. doi:10.1002/14651858.CD011134.pub2.
348. Niekel MC, Bipat S, Stoker J. Diagnostic imaging of colorectal liver metastases with CT, MR imaging, FDG PET, and/or FDG PET/CT: a meta-analysis of prospective studies including patients who have not previously undergone treatment. Radiology. Dec 2010;257(3):674-84. doi:10.1148/radiol.10100729.
349. Nikiforova MN, Mercurio S, Wald AI, et al. Analytical performance of the ThyroSeq v3 genomic classifier for cancer diagnosis in thyroid nodules. Cancer. Apr 15 2018;124(8):1682-1690. doi:10.1002/cncr.31245.
350. Nitz UA, Gluz O, Kummel S, et al. Endocrine Therapy Response and 21-Gene Expression Assay for Therapy Guidance in HR+/HER2- Early Breast Cancer. J Clin Oncol. Aug 10 2022;40(23):2557-2567. doi:10.1200/JCO.21.02759.
351. Nordstrom T, Vickers A, Assel M, Lilja H, Gronberg H, Eklund M. Comparison Between the Four-kallikrein Panel and Prostate Health Index for Predicting Prostate Cancer. Eur Urol. Jul 2015;68(1):139-46. doi:10.1016/j.eururo.2014.08.010.
352. Oken MM, Creech RH, Tormey DC, et al. Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol. Dec 1982;5(6):649-55.
353. Olleik G, Kassouf W, Aprikian A, et al. Evaluation of New Tests and Interventions for Prostate Cancer Management: A Systematic Review. J Natl Compr Canc Netw. Nov 2018;16(11):1340-1351. doi:10.6004/jnccn.2018.7055.
354. Ontario Health. Gene Expression Profiling Tests for Early-Stage Invasive Breast Cancer Health Technology Assessment. Ont Health Technol Assess Ser; 2020. p. 1-234.
355. Oto J, Fernandez-Pardo A, Royo M, et al. A predictive model for prostate cancer incorporating PSA molecular forms and age. Sci Rep. Feb 12 2020;10(1):2463. doi:10.1038/s41598-020-58836-4.
356. Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004;351(27):2817-2826. doi:10.1056/NEJMoa041588.
357. Paik S, Tang G, Shak S, et al. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol. Aug 10 2006;24(23):3726-34. doi:10.1200/JCO.2005.04.7985.
358. Pakkala S, Ramalingam SS. Personalized therapy for lung cancer: striking a moving target. JCI Insight. Aug 9 2018;3(15)doi:10.1172/jci.insight.120858.
359. Paniccia A, Polanco PM, Boone BA, et al. Prospective, Multi-Institutional, Real-Time Next-Generation Sequencing of Pancreatic Cyst Fluid Reveals Diverse Genomic Alterations That Improve the Clinical Management of Pancreatic Cysts. Gastroenterology. Jan 2023;164(1):117-133 e7. doi:10.1053/j.gastro.2022.09.028.
360. Pao W, Miller V, Zakowski M, et al. EGF receptor gene mutations are common in lung cancers from "never smokers" and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc Natl Acad Sci U S A. Sep 7 2004;101(36):13306-11. doi:10.1073/pnas.0405220101.
361. Park SJ, Lee MH, Kong SY, et al. Use of adjuvant chemotherapy in hormone receptor-positive breast cancer patients with or without the 21-gene expression assay. Breast Cancer Res Treat. Jul 2018;170(1):69-76. doi:10.1007/s10549-018-4740-z.
362. Patel VA, McCullum C, Sparks AD, Schmults CD, Arron ST, Jambusaria-Pahlajani A. Cutaneous squamous cell carcinoma staging may influence management in users: A survey study. Cancer Med. Jan 2021;11(1):94-103. doi:10.1002/cam4.4426.
363. Petit J, Carroll G, Zhao J, Roper E, Pockney P, Scott RJ. Evaluation of epigenetic methylation biomarkers for the detection of colorectal cancer using droplet digital PCR. Scientific Reports. 2023;13(1):8883. doi:10.1038/s41598-023-35631-5.
364. Pita-Fernandez S, Alhayek-Ai M, Gonzalez-Martin C, Lopez-Calvino B, Seoane-Pillado T, Pertega-Diaz S. Intensive follow-up strategies improve outcomes in nonmetastatic colorectal cancer patients after curative surgery: a systematic review and meta-analysis. Ann Oncol. Apr 2015;26(4):644-656. doi:10.1093/annonc/mdu543.
365. Pitman MB, Deshpande V. Endoscopic ultrasound-guided fine needle aspiration cytology of the pancreas: a morphological and multimodal approach to the diagnosis of solid and cystic mass lesions. Cytopathology. Dec 2007;18(6):331-47. doi:10.1111/j.1365-2303.2007.00457.x.
366. Pivot X, Mansi L, Chaigneau L, et al. In the era of genomics, should tumor size be reconsidered as a criterion for neoadjuvant chemotherapy? Oncologist. Apr 2015;20(4):344-50. doi:10.1634/theoncologist.2014-0198.
367. Pomponio M, Keele L, Hilt E, et al. Impact of 21-Gene Expression Assay on Clinical Outcomes in Node-Negative </= T1b Breast Cancer. Ann Surg Oncol. May 2020;27(5):1671-1678. doi:10.1245/s10434-019-08028-w.
368. Primrose JN, Perera R, Gray A, et al. Effect of 3 to 5 years of scheduled CEA and CT follow-up to detect recurrence of colorectal cancer: the FACS randomized clinical trial. JAMA. Jan 15 2014;311(3):263-70. doi:10.1001/jama.2013.285718.
369. Pugh SA, Shinkins B, Fuller A, Mellor J, Mant D, Primrose JN. Site and Stage of Colorectal Cancer Influence the Likelihood and Distribution of Disease Recurrence and Postrecurrence Survival: Data From the FACS Randomized Controlled Trial. Ann Surg. Jun 2016;263(6):1143-7. doi:10.1097/SLA.0000000000001351.
370. Rai BP, Luis Dominguez Escrig J, Vale L, et al. Systematic Review of the Incidence of and Risk Factors for Urothelial Cancers and Renal Cell Carcinoma Among Patients with Haematuria. Eur Urol. Aug 2022;82(2):182-192. doi:10.1016/j.eururo.2022.03.027.
371. Ransohoff DF. Rules of evidence for cancer molecular-marker discovery and validation. Nat Rev Cancer. 2004; 4:309–314.
372. Rath MG, Uhlmann L, Fiedler M, et al. Oncotype DX((R)) in breast cancer patients: clinical experience, outcome and follow-up-a case-control study. Arch Gynecol Obstet. Feb 2018;297(2):443-447. doi:10.1007/s00404-017-4618-z.
373. Reinert T, Henriksen TV, Christensen E, et al. Analysis of Plasma Cell-Free DNA by Ultradeep Sequencing in Patients With Stages I to III Colorectal Cancer. JAMA Oncol. Aug 1 2019;5(8):1124-1131. doi:10.1001/jamaoncol.2019.0528.
374. Riley JD, Procop GW, Kottke-Marchant K, Wylie R, Lacbawan FL. Improving Molecular Genetic Test Utilization through Order Restriction, Test Review, and Guidance. The Journal of Molecular Diagnostics. 2015;17(3):225-229. doi:10.1016/j.jmoldx.2015.01.003.
375. Robert NJ, Espirito JL, Chen L, et al. Biomarker testing and tissue journey among patients with metastatic non-small cell lung cancer receiving first-line therapy in The US Oncology Network. Lung Cancer. Apr 2022;166:197-204. doi:10.1016/j.lungcan.2022.03.004.
376. Roscher I, Falk R, Vos L, et al. Notice of Retraction and Replacement: Roscher et al. Validating 4 Staging Systems for Cutaneous Squamous Cell Carcinoma Using Population-Based Data: A Nested Case-Control Study. JAMA Dermatol. 2018;154(4):428-434.
377. Sahovaler A, Krishnan RJ, Yeh DH, et al. Outcomes of Cutaneous Squamous Cell Carcinoma in the Head and Neck Region With Regional Lymph Node Metastasis: A Systematic Review and Meta-analysis. JAMA Otolaryngol Head Neck Surg. Apr 1 2019;145(4):352-360. doi:10.1001/jamaoto.2018.4515.
378. Saleeby E, Bielinski K, Fitzgerald A, Siegel J, Ibrahim S. A Prospective, Multi-Center Clinical Utility Study Demonstrates that the 40-Gene Expression Profile (40-GEP) Test Impacts Clinical Management for Medicare-Eligible Patients with High-Risk Cutaneous Squamous Cell Carcinoma (cSCC). SKIN The Journal of Cutaneous Medicine. November 16, 2022, doi:10.25251
379. Saltman A, Zegar J, Haj-Hamed M, Verma S, Sidana A. Prostate cancer biomarkers and multiparametric MRI: is there a role for both in prostate cancer management? Ther Adv Urol. Jan-Dec 2021;13:1756287221997186. doi:10.1177/1756287221997186.
380. San Francisco IF, Rojas PA, Bravo JC, et al. Can We Predict Prostate Cancer Metastasis Based on Biomarkers? Where Are We Now? Int J Mol Sci. Aug 7 2023;24(15)doi:10.3390/ijms241512508.
381. Sarosdy M, Schellhammer P, Bokinsky G, et al. Clinical Evaluation of a Multi-target Fluorescent in Situ Hybridization Assay for Detection of Bladder Cancer. The Journal of Urology. 2002;168(5):1950-1954. doi:10.1097/01.ju.0000034254.89258.8e.
382. Sarosdy MF, Kahn PR, Ziffer MD, et al. Use of a multitarget fluorescence in situ hybridization assay to diagnose bladder cancer in patients with hematuria. J Urol. Jul 2006;176(1):44-7. doi:10.1016/S0022-5347(06)00576-3.
383. Savic S, Zlobec I, Thalmann GN, et al. The prognostic value of cytology and fluorescence in situ hybridization in the follow-up of nonmuscle-invasive bladder cancer after intravesical Bacillus Calmette-Guerin therapy. Int J Cancer. Jun 15 2009;124(12):2899-904. doi:10.1002/ijc.24258.
384. Schafer EJ, Jemal A, Wiese D, et al. Disparities and Trends in Genitourinary Cancer Incidence and Mortality in the USA. Eur Urol. Jul 2023;84(1):117-126. doi:10.1016/j.eururo.2022.11.023.
385. Schlomer BJ, Ho R, Sagalowsky A, Ashfaq R, Lotan Y. Prospective validation of the clinical usefulness of reflex fluorescence in situ hybridization assay in patients with atypical cytology for the detection of urothelial carcinoma of the bladder. J Urol. Jan 2010;183(1):62-7. doi:10.1016/j.juro.2009.08.157.
386. Schneider JE, Sidhu MK, Doucet C, Kiss N, Ohsfeldt RL, Chalfin D. Economics of cancer biomarkers. Per Med. 2012;9(8):829-837.
387. Schubeler D. Function and information content of DNA methylation. Nature. Jan 15 2015;517(7534):321-6. doi:10.1038/nature14192.
388. Seideman C, Canter D, Kim P, et al. Multicenter evaluation of the role of UroVysion FISH assay in surveillance of patients with bladder cancer: does FISH positivity anticipate recurrence? World J Urol. Sep 2015;33(9):1309-13. doi:10.1007/s00345-014-1452-9.
389. Shah PD, Patil S, Dickler MN, Offit K, Hudis CA, Robson ME. Twenty-one-gene recurrence score assay in BRCA-associated versus sporadic breast cancers: Differences based on germline mutation status. Cancer. Apr 15 2016;122(8):1178-84. doi:10.1002/cncr.29903.
390. Shahait M, Alshalalfa M, Nguyen PL, et al. Correlative analysis between two commercially available post-prostatectomy genomic tests. Prostate Cancer Prostatic Dis. Jun 2021;24(2):575-577. doi:10.1038/s41391-020-00305-0.
391. Shang D, Liu Y, Xu X, Chen Z, Wang D. Diagnostic value comparison of CellDetect, fluorescent in situ hybridization (FISH), and cytology in urothelial carcinoma. Cancer Cell Int. Sep 6 2021;21(1):465. doi:10.1186/s12935-021-02169-3.
392. Sheinson DM, Wong WB, Meyer CS, et al. Trends in Use of Next-Generation Sequencing in Patients With Solid Tumors by Race and Ethnicity After Implementation of the Medicare National Coverage Determination. JAMA Netw Open. 2021;4(12)doi:10.10001/jamanetworkopen.2021.38219.
393. Shinkins B, Nicholson BD, Primrose J, et al. The diagnostic accuracy of a single CEA blood test in detecting colorectal cancer recurrence: Results from the FACS trial. PLoS One. 2017;12(3):e0171810. doi:10.1371/journal.pone.0171810.
394. Shore N, Concepcion R, Saltzstein D, et al. Clinical utility of a biopsy-based cell cycle gene expression assay in localized prostate cancer. Curr Med Res Opin. Apr 2014;30(4):547-53. doi:10.1185/03007995.2013.873398.
395. Shu TD, Schumacher FR, Conroy B, et al. Disparities in cause-specific mortality by race and sex among bladder cancer patients from the SEER database. Cancer Causes Control. Jun 2023;34(6):521-531. doi:10.1007/s10552-023-01679-x.
396. Siegel JJ, Prasai A, Farberg AS, Goldberg MS. Performance and clinical decision-making using the prognostic 40-gene expression profile (40-GEP) test in 1,018 patients with high-risk cutaneous squamous cell carcinoma (SCC). SKIN. 2022 p. s96.
397. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer Statistics, 2021. CA Cancer J Clin. Jan 2021;71(1):7-33. doi:10.3322/caac.21654.
398. Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. Jan 2023;73(1):17-48. doi:10.3322/caac.21763.
399. Silvestri GA, Vachani A, Whitney D, et al. A Bronchial Genomic Classifier for the Diagnostic Evaluation of Lung Cancer. N Engl J Med. Jul 16 2015;373(3):243-51. doi:10.1056/NEJMoa1504601.
400. Simon R. Roadmap for developing and validating therapeutically relevant genomic classifiers. J Clin Oncol. Oct 10 2005;23(29):7332-41. doi:10.1200/JCO.2005.02.8712.
401. Singh G, Tolkachjov SN, Farberg AS. Incorporation of the 40-Gene Expression Profile (40-GEP) Test to Improve Treatment Decisions in High-Risk Cutaneous Squamous Cell Carcinoma (cSCC) Patients: Case Series and Algorithm. Clin Cosmet Investig Dermatol. 2023;16:925-935. doi:10.2147/CCID.S403330.
402. Singhi AD, Koay EJ, Chari ST, Maitra A. Early Detection of Pancreatic Cancer: Opportunities and Challenges. Gastroenterology. May 2019;156(7):2024-2040. doi:10.1053/j.gastro.2019.01.259.
403. Singhi AD, McGrath K, Brand RE, et al. Preoperative next-generation sequencing of pancreatic cyst fluid is highly accurate in cyst classification and detection of advanced neoplasia. Gut. Dec 2018;67(12):2131-2141. doi:10.1136/gutjnl-2016-313586.
404. Singhi AD, Zeh HJ, Brand RE, et al. American Gastroenterological Association guidelines are inaccurate in detecting pancreatic cysts with advanced neoplasia: a clinicopathologic study of 225 patients with supporting molecular data. Gastrointest Endosc. Jun 2016;83(6):1107-1117 e2. doi:10.1016/j.gie.2015.12.009.
405. Skacel M, Fahmy M, Brainard JA, et al. Multitarget fluorescence in situ hybridization assay detects transitional cell carcinoma in the majority of patients with bladder cancer and atypical or negative urine cytology. J Urol. Jun 2003;169(6):2101-5. doi:10.1097/01.ju.0000066842.45464.cc.
406. Snyder RA, Hu CY, Cuddy A, et al. Association Between Intensity of Posttreatment Surveillance Testing and Detection of Recurrence in Patients With Colorectal Cancer. JAMA. May 22 2018;319(20):2104-2115. doi:10.1001/jama.2018.5816.
407. Soares MO, Walker S, Palmer SJ, Sculpher MJ. Establishing the Value of Diagnostic and Prognostic Tests in Health Technology Assessment. Med Decis Making. May 2018;38(4):495-508. doi:10.1177/0272989X17749829.
408. Sobahy TM, Tashkandi G, Bahussain D, Al-Harbi R. Clinically actionable cancer somatic variants (CACSV): a tumor interpreted dataset for analytical workflows. BMC Med Genomics. Apr 25 2022;15(1):95. doi:10.1186/s12920-022-01235-7.
409. Society of Gynecologic Oncology. Multiplex Serum Testing for Women with Pelvic Mass. 2013.
410. Solin LJ, Gray R, Goldstein LJ, et al. Prognostic value of biologic subtype and the 21-gene recurrence score relative to local recurrence after breast conservation treatment with radiation for early stage breast carcinoma: results from the Eastern Cooperative Oncology Group E2197 study. Breast Cancer Res Treat. Jul 2012;134(2):683-92. doi:10.1007/s10549-012-2072-y.
411. Sommariva S, Tarricone R, Lazzeri M, Ricciardi W, Montorsi F. Prognostic Value of the Cell Cycle Progression Score in Patients with Prostate Cancer: A Systematic Review and Meta-analysis. Eur Urol. 2016;69(1):107-115. doi:10.1016/j.eururo.2014.11.038.
412. Song MJ, Lee HM, Kim SH. Clinical usefulness of fluorescence in situ hybridization for diagnosis and surveillance of bladder cancer. Cancer Genet Cytogenet. Apr 15 2010;198(2):144-50. doi:10.1016/j.cancergencyto.2010.01.007.
413. Soran A, Bhargava R, Johnson R, et al. The impact of Oncotype DX(R) recurrence score of paraffin-embedded core biopsy tissues in predicting response to neoadjuvant chemotherapy in women with breast cancer. Breast Dis. Jul 28 2016;36(2-3):65-71. doi:10.3233/BD-150199.
414. Sparano JA, Gray RJ, Makower DF, et al. Adjuvant Chemotherapy Guided by a 21-Gene Expression Assay in Breast Cancer. N Engl J Med. Jul 12 2018;379(2):111-121. doi:10.1056/NEJMoa1804710.
415. Sparano JA, Gray RJ, Makower DF, et al. Prospective Validation of a 21-Gene Expression Assay in Breast Cancer. N Engl J Med. Nov 19 2015;373(21):2005-14. doi:10.1056/NEJMoa1510764.
416. Springer S, Wang Y, Dal Molin M, et al. A combination of molecular markers and clinical features improve the classification of pancreatic cysts. Gastroenterology. Nov 2015;149(6):1501-10. doi:10.1053/j.gastro.2015.07.041.
417. Steele SR, Chang GJ, Hendren S, et al. Practice Guideline for the Surveillance of Patients After Curative Treatment of Colon and Rectal Cancer. Dis Colon Rectum. Aug 2015;58(8):713-25. doi:10.1097/DCR.0000000000000410.
418. Strande NT, Riggs ER, Buchanan AH, et al. Evaluating the Clinical Validity of Gene-Disease Associations: An Evidence-Based Framework Developed by the Clinical Genome Resource. Am J Hum Genet. Jun 1 2017;100(6):895-906. doi:10.1016/j.ajhg.2017.04.015.
419. Summers RJ, Castellino SM, Porter CC, et al. Comprehensive Genomic Profiling of High-Risk Pediatric Cancer Patients Has a Measurable Impact on Clinical Care. JCO Precis Oncol. Apr 2022;6:e2100451. doi:10.1200/PO.21.00451.
420. Sun F, Bruening W, Uhl S, Ballard R, Tipton K, Schoelles K. Quality, Regulation and Clinical Utility of Laboratory-developed Molecular Tests, in Quality, Regulation and Clinical Utility of Laboratory-developed Molecular Tests. Rockville, MD: Agency for Healthcare Research and Quality; 2010.
421. Swords RT, Azzam D, Al-Ali H, et al. Ex-vivo sensitivity profiling to guide clinical decision making in acute myeloid leukemia: A pilot study. Leuk Res. Jan 2018;64:34-41. doi:10.1016/j.leukres.2017.11.008.
422. Symonds EL, Hughes D, Flight I, et al. A Randomized Controlled Trial Testing Provision of Fecal and Blood Test Options on Participation for Colorectal Cancer Screening. Cancer Prev Res (Phila). Sep 2019;12(9):631-640. doi:10.1158/1940-6207.CAPR-19-0089.
423. Tanaka M, Fernandez-Del Castillo C, Kamisawa T, et al. Revisions of international consensus Fukuoka guidelines for the management of IPMN of the pancreas. Pancreatology. Sep-Oct 2017;17(5):738-753. doi:10.1016/j.pan.2017.07.007.
424. Tarazi W, Welch WP, Nguyen N, et al. Medicare Beneficiary Enrollment Trends and Demographic Characteristics. March 2022. Accessed December 19, 2024. https://aspe.hhs.gov/reports/medicare-enrollment.
425. Taylor J, Xiao W, Abdel-Wahab O. Diagnosis and classification of hematologic malignancies on the basis of genetics. Blood. Jul 27 2017;130(4):410-423. doi:10.1182/blood-2017-02-734541.
426. Tenny S, Hoffman MR. Prevalence. StatPearls Publishing LLC. 2023. https://www.ncbi.nlm.nih.gov/books/NBK430867/.
427. Teplitz R, Prado G, Litchman GH, Rigel DS. Impact of Gene Expression Profile Testing on the Management of Squamous Cell Carcinoma by Dermatologists. Journal of Drugs in Dermatology. 2019;18(10):980-984.
428. Tevis SE, Bassett R, Bedrosian I, et al. OncotypeDX Recurrence Score Does Not Predict Nodal Burden in Clinically Node Negative Breast Cancer Patients. Ann Surg Oncol. Mar 2019;26(3):815-820. doi:10.1245/s10434-018-7059-4.
429. Tie J, Cohen JD, Lahouel K, et al. Circulating Tumor DNA Analysis Guiding Adjuvant Therapy in Stage II Colon Cancer. N Engl J Med. Jun 16 2022;386(24):2261-2272. doi:10.1056/NEJMoa2200075.
430. Tie J, Cohen JD, Wang Y, et al. Circulating Tumor DNA Analyses as Markers of Recurrence Risk and Benefit of Adjuvant Therapy for Stage III Colon Cancer. JAMA Oncol. Dec 1 2019;5(12):1710-1717. doi:10.1001/jamaoncol.2019.3616.
431. Tie J, Wang Y, Tomasetti C, et al. Circulating tumor DNA analysis detects minimal residual disease and predicts recurrence in patients with stage II colon cancer. Sci Transl Med. Jul 6 2016;8(346):346ra92. doi:10.1126/scitranslmed.aaf6219.
432. Tipirneni R, Politi MC, Kullgren JT, Kieffer EC, Goold SD, Scherer AM. Association Between Health Insurance Literacy and Avoidance of Health cAre Services Owing to Cost. JAMA. 2018;1(7)doi:10.1001/jamanetworkopen.2018.4796.
433. Todenhofer T, Hennenlotter J, Esser M, et al. Stepwise application of urine markers to detect tumor recurrence in patients undergoing surveillance for non-muscle-invasive bladder cancer. Dis Markers. 2014;2014:973406. doi:10.1155/2014/973406.
434. Todenhofer T, Hennenlotter J, Esser M, et al. Combined application of cytology and molecular urine markers to improve the detection of urothelial carcinoma. Cancer Cytopathol. May 2013;121(5):252-60. doi:10.1002/cncy.21247.
435. Tomlins SA, Day JR, Lonigro RJ, et al. Urine TMPRSS2:ERG Plus PCA3 for Individualized Prostate Cancer Risk Assessment. Eur Urol. Jul 2016;70(1):45-53. doi:10.1016/j.eururo.2015.04.039.
436. Tramontano AC, Chen Y, Watson TR, et al. Pancreatic cancer treatment costs, including patient liability, by phase of care and treatment modality, 2000-2013. Medicine (Baltimore). Dec 2019;98(49):e18082. doi:10.1097/MD.0000000000018082.
437. Tsai HL, Huang CW, Chen CW, Yeh YS, Ma CJ, Wang JY. Survival in Resected Stage II Colorectal Cancer Is Dependent on Tumor Depth, Vascular Invasion, Postoperative CEA Level, and The Number of Examined Lymph Nodes. World J Surg. Apr 2016;40(4):1002-9. doi:10.1007/s00268-015-3331-y.
438. Turashvili G, Chou JF, Brogi E, et al. 21-Gene recurrence score and locoregional recurrence in lymph node-negative, estrogen receptor-positive breast cancer. Breast Cancer Res Treat. Nov 2017;166(1):69-76. doi:10.1007/s10549-017-4381-7.
439. Turashvili G, Gonzalez-Loperena M, Brogi E, et al. The 21-Gene Recurrence Score in Male Breast Cancer. Ann Surg Oncol. Jun 2018;25(6):1530-1535. doi:10.1245/s10434-018-6411-z.
440. Tycko B. Epigenetic gene silencing in cancer. J Clin Invest. Feb 2000;105(4):401-7. doi:10.1172/JCI9462.
441. US Preventive Services Task Force, Krist AH, Davidson KW, et al. Screening for Lung Cancer: US Preventive Services Task Force Recommendation Statement. JAMA. Mar 9 2021;325(10):962-970. doi:10.1001/jama.2021.1117.
442. Van Neste L, Hendriks RJ, Dijkstra S, et al. Detection of High-grade Prostate Cancer Using a Urinary Molecular Biomarker-Based Risk Score. Eur Urol. Nov 2016;70(5):740-748. doi:10.1016/j.eururo.2016.04.012.
443. Van Neste L, Partin AW, Stewart GD, Epstein JI, Harrison DJ, Van Criekinge W. Risk score predicts high-grade prostate cancer in DNA-methylation positive, histopathologically negative biopsies. Prostate. Sep 2016;76(12):1078-87. doi:10.1002/pros.23191.
444. Verberne CJ, Zhan Z, van den Heuvel E, et al. Intensified follow-up in colorectal cancer patients using frequent Carcino-Embryonic Antigen (CEA) measurements and CEA-triggered imaging: Results of the randomized "CEAwatch" trial. Eur J Surg Oncol. Sep 2015;41(9):1188-96. doi:10.1016/j.ejso.2015.06.008.
445. von Ahlfen S, Missel A, Bendrat K, Schlumpberger M. Determinants of RNA quality from FFPE samples. PLoS One. Dec 5 2007;2(12):e1261. doi:10.1371/journal.pone.0001261.
446. Wakefield E, Keller H, Mianzo H, Nagaraj CB, Tawde S, Ulm E. Reduction of Health Care Costs and Improved Appropriateness of Incoming Test Orders: the Impact of Genetic Counselor Review in an Academic Genetic Testing Laboratory. J Genet Couns. Sep 2018;27(5):1067-1073. doi:10.1007/s10897-018-0226-8.
447. Wang Y, Chang Q, Li Y. Racial differences in Urinary Bladder Cancer in the United States. Sci Rep. Aug 21 2018;8(1):12521. doi:10.1038/s41598-018-29987-2.
448. Wen HY, Krystel-Whittemore M, Patil S, et al. Breast carcinoma with an Oncotype Dx recurrence score <18: Rate of distant metastases in a large series with clinical follow-up. Cancer. Jan 1 2017;123(1):131-137. doi:10.1002/cncr.30271.
449. White J, Shenoy BV, Tutrone RF, et al. Clinical utility of the Prostate Health Index (phi) for biopsy decision management in a large group urology practice setting. Prostate Cancer Prostatic Dis. Apr 2017;21(1):78-84. doi:10.1038/s41391-017-0008-7.
450. Whitson J, Berry A, Carroll P, Konety B. A multicolour fluorescence in situ hybridization test predicts recurrence in patients with high-risk superficial bladder tumours undergoing intravesical therapy. BJU Int. Aug 2009;104(3):336-9. doi:10.1111/j.1464-410X.2009.08375.x.
451. Williams CP, Azuero A, Kenzik KM, et al. Guideline Discordance and Patient Cost Responsibility in Medicare Beneficiaries With Metastatic Breast Cancer. J Natl Compr Canc Netw. Oct 1 2019;17(10):1221-1228. doi:10.6004/jnccn.2019.7316.
452. Williams SB, Howard LE, Foster ML, et al. Estimated Costs and Long-term Outcomes of Patients With High-Risk Non-Muscle-Invasive Bladder Cancer Treated With Bacillus Calmette-Guerin in the Veterans Affairs Health System. JAMA Netw Open. Mar 1 2021;4(3):e213800. doi:10.1001/jamanetworkopen.2021.3800.
453. Wojno KJ, Costa FJ, Cornell RJ, et al. Reduced Rate of Repeated Prostate Biopsies Observed in ConfirmMDx Clinical Utility Field Study. Am Health Drug Benefits. 2014;7(3):129-134.
454. Woldu SL, Souter L, Boorjian SA, Barocas DA, Lotan Y. Urinary-based tumor markers enhance microhematuria risk stratification according to baseline bladder cancer prevalence. Urol Oncol. Nov 2021;39(11):787 e1-787 e7. doi:10.1016/j.urolonc.2021.03.022.
455. Woodward WA, Barlow WE, Jagsi R, et al. Association Between 21-Gene Assay Recurrence Score and Locoregional Recurrence Rates in Patients With Node-Positive Breast Cancer. JAMA Oncol. Apr 1 2020;6(4):505-511. doi:10.1001/jamaoncol.2019.5559.
456. Wylie D, Beaudenon-Huibregtse S, Haynes BC, Giordano TJ, Labourier E. Molecular classification of thyroid lesions by combined testing for miRNA gene expression and somatic gene alterations. J Pathol Clin Res. Apr 2016;2(2):93-103. doi:10.1002/cjp2.38.
457. Yadav S, Couch FJ. Germline Genetic Testing for Breast Cancer Risk: The Past, Present, and Future. Am Soc Clin Oncol Educ Book. Jan 2019;39:61-74. doi:10.1200/edbk_238987.
458. Yadav S, Hu C, Hart SN, et al. Evaluation of Germline Genetic Testing Criteria in a Hospital-Based Series of Women With Breast Cancer. J Clin Oncol. May 1 2020;38(13):1409-1418. doi:10.1200/jco.19.02190.
459. Yang DD, Buscariollo DL, Cronin AM, et al. Association between the 21-gene recurrence score and isolated locoregional recurrence in stage I-II, hormone receptor-positive breast cancer. Radiat Oncol. Aug 17 2020;15(1):198. doi:10.1186/s13014-020-01640-1.
460. Yang PS, Lee YH, Chung CF, et al. A preliminary report of head-to-head comparison of 18-gene-based clinical-genomic model and oncotype DX 21-gene assay for predicting recurrence of early-stage breast cancer. Jpn J Clin Oncol. Dec 18 2019;49(11):1029-1036. doi:10.1093/jjco/hyz102.
461. Yardley DA, Peacock NW, Shastry M, et al. A phase II trial of ixabepilone and cyclophosphamide as neoadjuvant therapy for patients with HER2-negative breast cancer: correlation of pathologic complete response with the 21-gene recurrence score. Breast Cancer Res Treat. Nov 2015;154(2):299-308. doi:10.1007/s10549-015-3613-y.
462. Yoder BJ, Skacel M, Hedgepeth R, et al. Reflex UroVysion testing of bladder cancer surveillance patients with equivocal or negative urine cytology: a prospective study with focus on the natural history of anticipatory positive findings. Am J Clin Pathol. Feb 2007;127(2):295-301. doi:10.1309/ADJL7E810U1H42BJ.
463. Young PE, Womeldorph CM, Johnson EK, et al. Early detection of colorectal cancer recurrence in patients undergoing surgery with curative intent: current status and challenges. J Cancer. 2014;5(4):262-71. doi:10.7150/jca.7988.
464. Yu-qing Y, Lei W, Mei-ling H. Clinical significance of 21-gene recurrence score assay for hormone receptor–positive, lymph node-negative breast cancer in early stage. Experimental and Molecular Pathology. 2019;108:150-155. doi:10.1016/j.yexmp.2019.04.013.
465. Zacharias M, Absenger G, Kashofer K, et al. Reflex testing in non-small cell lung carcinoma using DNA- and RNA-based next-generation sequencing-a single-center experience. Transl Lung Cancer Res. Nov 2021;10(11):4221-4234. doi:10.21037/tlcr-21-570.
466. Zakria D, Brownstone N, Berman B, et al. Incorporating a Prognostic Gene Expression Profile Test into the Management of Cutaneous Squamous Cell Carcinoma: An Expert Consensus Panel Report. J Drugs Dermatol. Dec 1 2023;22(12):7691. doi:10.36849/jdd.7691.
467. Zappala SM, Scardino PT, Okrongly D, Linder V, Dong Y. Clinical performance of the 4Kscore Test to predict high-grade prostate cancer at biopsy: A meta-analysis of us and European clinical validation study results. Rev Urol. 2017;19(3):149-155. doi:10.3909/riu0776.
468. Zhang J, Wang Y, Wijaya WA, Liang Z, Chen J. Efficacy and prognostic factors of adjuvant radiotherapy for cutaneous squamous cell carcinoma: A systematic review and meta-analysis. J Eur Acad Dermatol Venereol. Sep 2021;35(9):1777-1787. doi:10.1111/jdv.17330.
469. Zhang L, Hsieh MC, Petkov V, Yu Q, Chiu YW, Wu XC. Trend and survival benefit of Oncotype DX use among female hormone receptor-positive breast cancer patients in 17 SEER registries, 2004-2015. Breast Cancer Res Treat. Apr 2020;180(2):491-501. doi:10.1007/s10549-020-05557-x.
470. Zhang Y, Hong YK, Zhuang DW, He XJ, Lin ME. Bladder cancer survival nomogram: Development and validation of a prediction tool, using the SEER and TCGA databases. Medicine (Baltimore). Nov 2019;98(44):e17725. doi:10.1097/MD.0000000000017725.