PROPOSED Local Coverage Determination (LCD)

MolDX: Molecular Biomarkers for Risk Stratification of Indeterminate Pulmonary Nodules Following Bronchoscopy

DL39658

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Proposed LCDs are works in progress that are available on the Medicare Coverage Database site for public review. Proposed LCDs are not necessarily a reflection of the current policies or practices of the contractor.

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MolDX: Molecular Biomarkers for Risk Stratification of Indeterminate Pulmonary Nodules Following Bronchoscopy
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Issue

Issue Description

This LCD outlines limited coverage for this service with specific details under Coverage Indications, Limitations and/or Medical Necessity.

Issue - Explanation of Change Between Proposed LCD and Final LCD

CMS National Coverage Policy

Title XVIII of the Social Security Act, §1862(a)(1)(A) allows coverage and payment for only those services that are considered to be reasonable and necessary.

42 CFR §410.32(a) Diagnostic x-ray tests, diagnostic laboratory tests, and other diagnostic tests: Conditions

CMS Internet-Only Manual, Pub. 100-02, Medicare Policy Manual, Chapter 15, §80 Requirements for Diagnostic X-Ray, Diagnostic Laboratory, and Other Diagnostic Tests, §80.1.1 Certification Changes

Coverage Guidance

Coverage Indications, Limitations, and/or Medical Necessity

This contractor will provide limited coverage for molecular tests to aid in the diagnosis or exclusion of lung cancer in a patient with an indeterminate pulmonary nodule (IPN) following a non-diagnostic bronchoscopy when ALL of the following conditions are met:

  1. The beneficiary has undergone bronchoscopy for an indeterminate pulmonary nodule AND
    1. The bronchoscopy has failed to provide a specific histopathological diagnosis such that further diagnostic procedures are considered necessary to pursue a specific diagnosis (non-diagnostic bronchoscopy); AND
    2. Test results will be used to meaningfully inform patient management within the framework of nationally recognized consensus guidelines.
    3. If medically reasonable and necessary following established guidelines, the nodule cannot be evaluated by an alternate methodology (EBUS, FNA, etc.) for a specific diagnosis.
  2. The beneficiary does NOT have any of the following:
    1. Personal history of cancer
    2. Current diagnosis of cancer or high clinical suspicion for cancer
    3. An overall low risk for pulmonary malignancy such that test results would not meaningfully alter patient management and significantly improve patient outcomes.
    4. An overall high risk for pulmonary malignancy such that test results would not meaningfully alter patient management and significantly improve patient outcomes.
  3. The beneficiary has not been tested with the same or similar assay for the same clinical indication.
  4. The beneficiary is within the population and has the indication for which the test was developed and is covered. The lab providing the test is responsible for clearly indicating to treating clinicians the population and indication for test use.
  5. The test has demonstrated clinical validity and utility, establishing a clear and significant biological/molecular basis for stratifying patients and subsequently selecting (either positively or negatively) a clinical management decision in a clearly defined population.
  6. Clinical validity of any analytes (or expression profiles) measured must be established through a study published in the peer-reviewed literature for the intended use of the test in the intended population.
  7. Rule-out tests should have a high sensitivity and negative predictive value (NPV) such that patients can be safely selected for a less aggressive management strategy without delay to diagnosis due to false negative results.
  8. Rule-in tests should have a high specificity and positive predictive value (PPV) such that patients can be safely selected for more aggressive management without significantly increasing procedures in patients without cancer due to false positive results.
  9. The test demonstrates analytical validity including both analytical and clinical validations. If the test relies on an algorithm (which may range in complexity from a threshold determination of a single numeric value to a complex mathematical or computational function), the algorithm must be validated in a cohort that is not a development cohort for the algorithm.
  10. Tests utilizing a similar methodology or evaluating a similar molecular analyte to a test for which there is a generally accepted testing standard or for which existing coverage exists must demonstrate equivalent or superior test performance (i.e., sensitivity and/or specificity) when used for the same indication in the same intended-use population. New tests that become available with significantly improved performance may render older tests no longer compliant with this policy.
  11. The test successfully completes a Molecular Diagnostic Services Program (MolDX®) technical assessment that ensures the test is reasonable and necessary as described above.

NOTE: Next Generation Sequencing (NGS) performed to identify genetic variants in samples classified as malignant is not within the scope of this policy but may fall under other established policies.

Summary of Evidence

Lung Cancer Screening

Lung cancer is the leading cause of cancer-related deaths in the United States.1 It is estimated that 236,740 new cases of lung and bronchial cancer will have been diagnosed in the United States in 2022 (117,910 in men and 118,830 in women) with 130,180 anticipated deaths (68,820 in men and 61,360 in women).1 Earlier diagnosis has significant impact on clinical outcome, as 5-year survival increases from 6%-9.5% for distant-stage disease, to 33-44% for regional disease, and 60-75% for localized disease,1 although there have been recent improvements in non-small cell lung cancer outcomes across stages with the advent of targeted and molecular therapies.2 Data from larger randomized control trials such as the National Lung Screening Trial (NLST) and the Dutch-Belgian Randomized Lung Cancer Screening Trial (Nederlands–Leuvens Longkanker Screenings Onderzoek [NELSON]) supports low-dose CT (LDCT) screening in high-risk individuals based on smoking criteria and age.3-5 A noticeable decline in advanced-stage lung cancer diagnosis with a corresponding increase in incidence of localized stage disease was observed between 2013 and 2018 following recommendation for lung cancer screening by the United States Preventive Services Task Force (USPSTF).6 Due to increased strength of evidence supporting annual screening with LDCT for high-risk individuals, USPSTF issued updated screening guidelines in March of 2021, expanding eligibility to adults aged 50 to 80 years who have a 20 pack-year smoking history and currently smoke or have quit within the past 15 years.6 The Centers for Medicare & Medicaid Services (CMS) covers annual LDCT screening for appropriate Medicare beneficiaries with significant smoking history up to 77 years of age if they participate in shared decision-making before their first screening LDCT.7 LDCT screening is also recommended by the 2021 CHEST Guideline and Expert Panel Report on Screening for Lung Cancer.8

Although there is consensus on the value of LDCT screening, uncertainty remains about the appropriate duration of screening and age of screening cessation, with NCCN recommending annual screening until the patient is no longer a candidate for definitive treatment.9 There are also potential risks associated with LDCT screening including false negative as well as false positive results that can lead to unnecessary tests and invasive procedures, complications from the diagnostic workup, overdiagnosis of incidental findings, short-term anxiety due to indeterminate results, and radiation exposure.9 As a result of increased implementation of screening guidelines, the incidence of nodules detected on CT continues to rise, and an estimated 1.5 million nodules are detected each year in the United States.10 Most lung nodules found on LDCT are benign,3,4 such that lung cancer prevalence in the screening setting is 0.8-2.2% and approximately 0.11% in nodules incidentally detected when imaging is performed for other reasons.10 An LDCT screen is defined as “positive” if the size and morphologic features of the detected nodule results in a recommendation for follow-up testing in addition to recommended annual screening based on published guidelines.8 Approximately 7% of patients with false positive results go on to an invasive procedure, most often bronchoscopy.9,11,12

Indeterminate Pulmonary Nodules (IPN) Risk Assessment and Management Guidelines

The American College of Chest Physicians (ACCP) Evidence-Based Clinical Practice Guidelines define an indeterminate nodule as any nodule that is not calcified in a benign pattern and is lacking clearly benign features such as intramodular fat indicative of hamartoma or a feeding artery and vein typical for arteriovenous malformation.13 Factors such as nodule morphology, size, attenuation, and clinical context harbor varying risks of malignancy and the probability of malignancy (low, intermediate, high) determines further management, which is often either continued CT-surveillance PET/CT, and tissue sampling including percutaneous needle biopsy, bronchoscopic biopsy, or surgical biopsy.14,9 Nodule attenuation (solid, part-solid, ground glass opacity), size (diameter or volume), and rate of growth factor strongly into multiple management guidelines.13,15,16,17,9 NCCN guidelines delineate cut-off values for size, follow-up interval, and intervention depending on nodule appearance as solid, part-solid, or non-solid on initial LDCT screen and subsequently consider level of clinical suspicion of lung cancer.9 The Fleischner Society Guidelines for nodules discovered outside of LDCT cancer screening also propose follow-up methods depending on whether a nodule is solid or sub-solid. Additional factors considered for management include the number of nodules, and size or volume of each nodule along with clinical risk factors.15 The ACCP outlines a similar methodology for indeterminate nodules discovered in the screening setting as well as those detected incidentally wherein the follow-up strategy is dependent on nodule appearance, size, and risk or probability of malignancy.13

Clinical risk factors associated with a higher risk of malignancy include cigarette smoking, age, occupational and environmental exposures, pulmonary fibrosis, chronic obstructive pulmonary disorder (COPD), personal history of lung cancer, and female sex.14 ACCP Practice Guidelines recommend that clinicians estimate the pretest probability of malignancy either qualitatively by using their clinical judgement and/or quantitatively by using a validated model. Pretest probability of malignancy enables selection and subsequent interpretation of diagnostic tests as depicted in Table 1 below.13 Several quantitative risk models exist to facilitate decision-making by incorporating clinical and radiologic factors into a single risk score that summarizes likelihood of malignancy.18-20 These models can be utilized alongside clinical judgement and recommended guidelines to guide the next management step.14 The Herder model incorporates PET avidity into the Mayo Clinic Model to improve diagnostic value,21 whereas the TREAT model was designed for use in the surgical clinic and incorporates hemoptysis and PET avidity, assuming a higher prevalence of malignancy.22 Risk calculator performance varies depending on the lung cancer prevalence and clinical characteristics of the original study population.23 Therefore, risk estimates should be interpreted with caution, taking the patient’s clinical context and factors such as geographical location into consideration. Recent studies suggest that physicians generally have good intuition regarding risk assessment of IPNs and while many do not document a quantitative prediction of malignancy in advance of tissue diagnosis, qualitative risk statements generally correlate with quantitative risk.25 NCCN Guidelines for Lung Cancer Screening encourage a multidisciplinary approach to evaluation for the suspicion of lung cancer including input from thoracic radiology, pulmonary medicine, and thoracic surgery, and may include the use of a lung nodule risk calculator to inform probability assessment. NCCN cautions that risk calculator use is not a substitute for multidisciplinary pulmonary management as geographic and other factors can influence calculator accuracy.9

Table 1. ACCP: Assessment of the Probability of Malignancy.13

Assessment Criteria

Probability of Malignancy

Low (<5%)

Intermediate (5%-65%)

High (>65%)

Clinical factors alone (determined by clinical judgement and/or use of a validated model)

Young, less smoking, no prior cancer, smaller nodule size, regular margins, and/or non-upper-lobe location

Mixture of low and high probability features

Older, heavy smoking, prior cancer, larger size, irregular/spiculated margins, and/or upper-lobe location

FDG-PET scan results

Low-moderate clinical probability and low FDG-PET activity

Weak or moderate FDG-PET scan activity

Intensely hypermetabolic nodule

Nonsurgical biopsy results (bronchoscopy or TTNA)

Specific benign diagnosis

Nondiagnostic

Suspicious for malignancy

CT scan surveillance

Resolution or near-complete resolution, progressive or persistent decrease in size, or no growth over ≥ years (solid nodule) or ≥ 3-5 years (subsolid nodule)

NA

Clear evidence of growth

FDG = fluorodeoxyglucose; NA = not applicable; TTNA = transthoracic needle aspiration

Guidelines largely agree on management of low and high risk IPNs.9,13-17 Nodules with low probability of malignancy should be monitored with CT surveillance, whereas those with high probability of malignancy merit more aggressive evaluation and consideration for surgical resection.26 However, there is heterogeneity in management recommendations of IPNs with intermediate malignancy risk.14 Additional evaluation options for intermediate nodules include short-term interval CT, fluorodeoxyglucose (FDG) PET, non-surgical or surgical biopsy depending on the patient’s risk of malignancy, health status, preference, and clinical setting.14 Studies have also shown that many physicians do not follow management guidelines, introducing further heterogeneity in management and potential for suboptimal care with inadequate work-up, prolonged surveillance or potentially harmful unnecessary procedures.24,27 For example, a study of pulmonary nodule evaluation in the usual care setting outside of a lung cancer screening study or dedicated pulmonary nodule clinic found 18% of patients receiving over-evaluation consisting of prolonged surveillance and biopsy and 27% receiving less intense evaluation than recommended by guidelines, with radiologist recommendation as the strongest predictor of intensity of evaluation.27 However, standardized reporting of radiologic findings has helped to increase guideline compliance.27,28 Additional improvement to risk stratification beyond the available clinical and radiologic features is needed to decrease unnecessary biopsy referrals and costs, especially in the intermediate risk group that is most likely to undergo further testing for benign disease.

Bronchoscopy

Bronchoscopy is a common approach to tissue sampling and is frequently used in patients with IPNs and an intermediate-risk of malignancy, for whom guidelines show heterogeneity in management recommendations ranging from CT-surveillance to non-surgical biopsy. Bronchoscopy is a relatively safe procedure with less than 1% of cases complicated by pneumothorax.29 Approximately 500,000 bronchoscopies are performed annually in the United States of which approximately half are done for lung cancer evaluation.30 Nevertheless, bronchoscopy has lower sensitivity for smaller and peripherally-located nodules and up to 40% of bronchoscopies lead to a non-diagnostic outcome wherein the clinician cannot obtain a clinically actionable benign or malignant diagnosis. Physicians are subsequently faced with the dilemma of whether to monitor such patients with CT surveillance or proceed to a surgical lung biopsy or transthoracic needle biopsy associated with a greater risk of morbidity, such as pneumothorax or hemorrhage, or mortality.31

Biomarkers to Improve Diagnostic Yield of Bronchoscopy

Biomarkers can serve as an adjunct to bronchoscopy by resolving equivocal cytology32 or improving risk-stratification to inform further patient management. Recently published examples include detection of cancer-associated deoxyribonucleic acid (DNA) methylation and gene mutations in bronchial washings performed during fiberoptic bronchoscopy for diagnosis of lung cancer,33 development of an exploratory bronchoalveolar lavage (BAL) genomic classifier aimed at detecting tumor-derived mutations by targeted sequencing of BAL cell free DNA (cfDNA),34 and validation of a multiple logistic regression model including methylated tumor DNA from the homeobox A9 (HOXA) gene in bronchial lavage along with clinical factors such as age and smoking status as a supplementary diagnostic tool for lung cancer detection,35 among others. While many approaches are in the research and development pipeline, few have been commercialized and incorporated into clinical practice36 as further studies are required to determine clinical validity and utility. This evidence summary will focus on in-depth review of commercialized molecular assays that aim to improve risk stratification of indeterminate pulmonary nodules following non-diagnostic bronchoscopy, acknowledging that additional molecular assays exist in various stages of development.

First Generation Gene Expression Profiling (GEP) Test: Percepta Bronchial Genomic Classifier (BGC)

The Percepta BGC is a messenger-ribonucleic acid (RNA) assay performed on cytology brushings of bronchial epithelial cells collected during bronchoscopy from current or former smokers undergoing evaluation for suspected lung cancer and is performed in the event of non-diagnostic bronchoscopy. The test uses patient age along with microarray technology to measure the expression of 23 lung cancer associated genes to improve the diagnostic yield of bronchoscopy. The assay was designed as a high sensitivity “rule-out” test for patients with a non-diagnostic bronchoscopy and intermediate-risk of malignancy, in order to re-classify intermediate pre-test cancer risk to low post-test risk with a nondiagnostic bronchoscopy and negative classifier result.31

BGC classifier development was rooted in preceding studies that demonstrated a molecular field of injury consisting of gene expression changes in airway epithelial cells as a function of cigarette smoking.37,38 The classifier was developed in current or former smokers undergoing bronchoscopy for suspected lung cancer across 28 centers in the United States, Canada, and Ireland in two independent, prospective, multicenter observational studies, known as the Airway Epithelial Gene Expression in the Diagnosis of Lung Cancer (AEGIS-1 and AEGIS-2) Trials.31 Exclusion criteria consisted of age <21 years, smoking <100 cigarettes, concurrent cancer or history of lung cancer. The prevalence of lung cancer in AEGIS-1 was 74% and 78% in AEGIS-2. The median age of patients in AEGIS-1 was 62 years (interquartile range 55-70) and 64 years (interquartile range 57-71) in AEGIS-2.31 Both trials included a diverse group of patients, with nearly 20% being African American.31 A set of patients from AEGIS-1 (223 patients diagnosed with lung cancer and 76 patients diagnosed with benign disease) was randomly selected for classifier training39 whereas 298 patients from AEGIS-1 and 341 patients from AEGIS-2 were utilized in classifier validation.31

In the validation set, 43% of bronchoscopies were non-diagnostic for lung cancer (95% Confidence interval (95% CI), 39-46), including for 25% of patients in whom lung cancer was ultimately diagnosed (95% CI, 21-29). A “diagnostic” bronchoscopy was defined to yield a confirmed lung cancer diagnosis. Patients’ pre-bronchoscopy risk of malignancy was assessed by each treating physician based on their subjective assessment and the results were divided into categories of low (<10%), intermediate (10-60%), and high (>60%) probability of malignancy with a corresponding malignancy prevalence of 5%, 41%, and 95%, respectively. Physicians and patients were not informed of classifier results. Bronchoscopy sensitivity for lung cancer detection was 74% (95% CI, 68 to 79) in AEGIS-1 and 76% (95% CI, 71 to 81) in AEGIS-2. In AEGIS-1, the area under the receiver-operating-characteristic curve (AUC) for the classifier was 0.78 (95% CI, 0.73 to 0.83) with a sensitivity of 88% (95% CI, 83 to 92) and a specificity of 47% (95% CI, 37 to 58). The AUC of the classifier in AEGIS-2 was 0.74 (95% CI, 0.68 to 0.80), with a sensitivity of 89% (95% CI, 84 to 92), and a specificity of 47% (95% CI, 36 to 59). There was no statistically significant difference in classifier performance in AEGIS-1 vs AEGIS-2. Combining bronchoscopy with the classifier led to an increased sensitivity of 96% (95% CI, 93 to 98) in AEGIS-1 and 98% (95% CI, 96 to 99) in AEGIS-2, independent of lesion size and location. The negative predictive value (NPV) of the classifier in 101 patients with an intermediate pretest probability of cancer and nondiagnostic bronchoscopy was 91% (95% CI, 75 to 98),31 whereas in 426 patients with a high pretest probability of cancer, the NPV was 38% (95% CI, 15-65).31

Overall, 487 patients were diagnosed with lung cancer and 120 had non-diagnostic bronchoscopy, of which 13 were classifier negative (false negatives). Three of these were in the intermediate risk category and 10 had a high pretest risk of malignancy. Patients with false negative results should not experience significant delay to diagnosis as patients with a nondiagnostic bronchoscopy and negative classifier score should undergo CT surveillance as standard of care when an immediate invasive strategy is not utilized.31 The positive predictive value (PPV) in the intermediate risk population was 40% (95% CI 27-55) and 84% (95% CI 75-91) in the high pretest probability group. Given the modest PPV in the intermediate risk group (40%), the authors concluded that a positive classifier result does not warrant decision alteration between invasive strategy and imaging-surveillance.31 A negative classifier result has potential clinical utility in patients with a nondiagnostic bronchoscopy and an intermediate probability of cancer, as a negative classifier score may warrant a more conservative diagnostic strategy involving imaging surveillance instead of invasive procedures as the next step in patient management.31 Analytical performance of the bronchial genomic classifier was reported in Hu et al40.

Clinical utility was evaluated by examining potential procedure reduction in the AEGIS trials as a result of classifier use41 and through a survey of pulmonary physicians presented with clinical cases.42 The Percepta Registry Cohort was established as a multicenter prospective registry including academic and community medical centers aimed at observing physician management of patients with pulmonary nodules following nondiagnostic bronchoscopy in a setting with and without classifier results.43 Lee et al43 found that 34.3% of patients with low or intermediate risk of malignancy and a clinician-designated plan for a subsequent invasive procedure had a reduction in malignancy risk and 73.9% of these patients had a subsequent change in management plan from invasive procedure to surveillance with the majority avoiding a procedure up to 12 months following the initial evaluation. The study did not find a statistically significant delay to diagnosis in the classifier false negative patients nor a significant increase in advanced cancer stage at diagnosis.43

Second Generation GEP: Percepta Genomic Sequencing Classifier (GSC)

The Percepta Genomic Sequencing Classifier (GSC) is a second-generation classifier currently offered for clinical use in replacement of the BGC. It was developed from whole transcriptome RNA sequencing along with clinical factors, with multiple thresholds allowing for both up-classification and down-classification of malignancy risk in patients with non-diagnostic biopsy.8,44 The up-classification is intended to be an improvement on the first generation Bronchial Genomic Classifier, which was designed to be solely a “rule out” test for intermediate-risk patients. The GSC final model uses 1232 genes and four clinical covariates – including pack-years, age, and interfering factors such as inhaled medication use and specimen collection timing.44 The GSC was developed using samples from current and former smokers who underwent bronchoscopy for suspected lung cancer as part of the AEGIS-1 and AEGIS-2 trials and the BGC Registry, wherein patients were split into training and validation cohorts. Classifier performance characteristics from the validation set (N=412) are listed in Table 2 below, demonstrating similar performance as a rule-out test for intermediate and low risk categories compared with the first generation BGC.

Table 2. Lung cancer genomic sequencing classifier validation performance.44

AUC

Pre-test Cancer Risk

Cancer prevalence

Cancer Risk re-stratification

Specificity (% and 95% CI)

Sensitivity (% and 95% CI)

Post-test NPV/PPV (% and 95 CI)

% Re-stratified

 

 

73.4% [95% CI 68.3-78.4]

Low

5%

Low to Very Low

57.4% [44.8-69.3]

100% [39.8-100]

100% NPV [91.0-100]

54.5%

Intermediate

28.2%

Intermediate to Low

37.3% [27.9-47.4]

90.6% [79.3-96.9]

91.0% NPV [80.8-96.0]

29.4%

Intermediate to High

94.1% [87.6-97.8]

28.3% [16.8-42.3]

65.4% PPV [43.8-82.1]

12.2%

High

73.6%

High to Very High

91.2% [76.3-98.1]

34.0% [25.0-43.8]

91.5% PPV [77.9-97.0]

27.3%

 

29.4% of intermediate-risk patients had an “actionable negative” result, such that if the test were to lead to surveillance imaging in 10 patients, 9 would be expected to have benign lesions and safely avoid further testing, whereas one patient with a malignant lesion could potentially experience a delay in further evaluation.44 12.2% of the intermediate cohort was up-classified form intermediate to high risk with a PPV of 65.4%. Thus, if the test were to result in more aggressive management, approximately two patients with malignancy would experience additional invasive testing or treatment, whereas one patient with a benign lesion would do the same.44 The potential impact of the classifier on the rate of invasive procedures was assessed in the AEGIS I and II cohorts.45 This was done by estimating the potential reduction in the number of procedures in patients who have been re-classified by the GSC, assuming that the classifier would have been used in procedure decision-making.45 As a result of classifier use, 50% of patients with benign lesions as well as 29% of those with malignancy undergoing additional invasive procedures prior to definitive surgery could have potentially avoided invasive procedures.44,45 Analytical validation of the GSC was published by Johnson et al46.

Raval et al47 conducted an observational study that retrospectively assessed data from four clinical sites (two academic and two community medical centers) that regularly use the GSC in clinical practice. 42% of patients had a change in risk category following GSC results compared to pre-procedure risk of malignancy. Potential clinical utility was modeled using performance characteristics from prior studies and modeling was based on hypothetical assumptions that risk up-classification from high to very high would lead to referral for surgical resection as the next management step and down-classification to low or very low risk of malignancy would result in CT surveillance as the next step.47 The authors aim to update this study to assess the true impact of Percepta GSC results in the context of outcomes data including future diagnosis of cancer or benign disease when that information becomes available.47 Finally, Sethi et al48 reported results from a decision impact study demonstrating that up-classification of malignancy risk from high to very high can potentially allow more patients to proceed more rapidly to curative therapy, with a decrease in intervening diagnostic procedures.

Analysis of Evidence (Rationale for Determination)

Accurate malignancy risk stratification is essential to guide diagnostic evaluation of IPNs in a manner that maximizes diagnosis of malignancy while simultaneously minimizing unnecessary risk, associated anxiety, and cost of benign IPN evaluation.14 Guidelines on the management of intermediate risk patients with IPNs show heterogeneous recommendations. Moreover, management options in these patients include bronchoscopy, an invasive procedure with an overall diagnostic yield of 69% according to a recent study.52 Therefore, whenever possible, IPNs in these patients should be investigated using noninvasive procedures in order to avoid morbidity in patients without cancer.9,49 Molecular biomarkers can fulfill this need.

The evidence published to date has demonstrated clinical utility and validity of molecular biomarkers as high sensitivity “rule-out” tests for current or former smokers with indeterminate pulmonary nodules and intermediate risk of malignancy who have undergone a non-diagnostic bronchoscopy. These tests can effectively re-classify risk of malignancy from pre-test “intermediate” to post-test “low” risk in patients with a non-diagnostic bronchoscopy and negative classifier result. This re-classification can inform the management of post-test low-risk patients, enabling selection of CT-surveillance as the immediate follow-up to a non-diagnostic biopsy versus additional invasive tissue sampling. The clinical validity was effectively demonstrated by Silvestri et al31, wherein use of the Percepta BGC increased the diagnostic yield of bronchoscopy such that the combination of bronchoscopy with the BGC classifier led to an increased sensitivity of 96% (95% CI, 93 to 98) in the AEGIS-1 and 98% (95% CI, 96 to 99) in the AEGIS-2 trials, independent of lesion size and location. In comparison, the sensitivity for lung cancer detection of bronchoscopy alone was 74% (95% CI, 68 to 79) in AEGIS-1 and 76% (95% CI, 71 to 81) in AEGIS-2. The NPV of the classifier in 101 patients with an intermediate pretest probability of cancer and nondiagnostic bronchoscopy was 91% (95% CI, 75 to 98).31 Similar classifier performance was demonstrated by the second generation Percepta GSC test.44

In patients with an intermediate pretest probability of cancer, the AEGIS studies reported only three false negative classifier results. Patients with false negative “low risk” classifier results should not experience significant delay to diagnosis as patients with a nondiagnostic bronchoscopy and negative classifier score should still undergo CT surveillance as a standard of care when an immediate additional invasive strategy is not utilized.43 This was supported by lack of significant delay to diagnosis in classifier negative patients and no significant increase in advanced cancer stage at diagnosis in the prospective multicenter study conducted by Lee et al using the Percepta Registry Cohort.43 This study demonstrated clinical utility through reduction in procedure recommendations in patients with nondiagnostic biopsy and negative classifier results.43 Nevertheless, there are several important limitations. Namely, the study was not a randomized comparison of management and outcomes in patients whose care was guided by use of the Percepta BGC to those whose care was not guided by classifier results. There was also variability in practice patterns amongst the medical sites included in the Percepta Registry, in that some sites chose close surveillance as a result of Percepta down-classification, whereas others retained their post-test plan for invasive procedures in this patient group. Furthermore, a significant number of patients lacked consensus in adjudicated final diagnosis following expert panel review, most often as a result of insufficient clinical information at follow-up, introducing a potential source of selection bias.43 Additional confounding factors include the timing of specimen collection, in that 97% of patients in the AEGIS cohorts had sample collection prior to other cytology/pathology sampling whereas only 38% of BGC registry samples were collected prior to other testing (p<0.001), introducing potential confounding from gene expression signatures resulting from tissue injury, which was addressed by the second generation Percepta GSC test.44 Finally, the study authors note that the observed classifier impact could have been greater if clinical sites had experience using the test prior to study enrollment.

The studies described herein have demonstrated that molecular tests with high sensitivity and NPV can be used to rule out malignancy in current or former smokers with intermediate risk lung nodules and a nondiagnostic bronchoscopy to meaningfully reduce the number of procedures in patients without lung cancer. However, their ability to rule-in malignancy is not as clear. The moderate PPV of 65% in the intermediate risk category observed in the GSC could lead to increased procedures in one out of three patients without malignancy, while potentially expediting malignancy diagnosis in two, when compared to the standard of care. The clinical utility of this risk/benefit trade-off remains to be demonstrated. The benefit of testing patients with a low pre-test risk of malignancy for whom CT surveillance is the recommended follow-up strategy according to practice guidelines is also unclear, as re-stratification to a very low risk category leads to the same recommended intervention (i.e. CT surveillance).

Current clinical guidelines show solid performance in the appropriate clinical context, in that detected suspicious nodules are followed in a manner that minimizes extensive stage-shift with concurrent effort at reduction in invasive procedures in patients with benign lesions.50 Molecular biomarkers must demonstrate clear value of re-stratification beyond currently available clinical and radiologic strategies, such that the post-test risk of malignancy results in impactful changes in patient management with improved outcomes.51 All tests should be clinically validated in their intended-use population and will be deemed reasonable and necessary for clinical use only in that population. Patients whose pretest risk of malignancy is too low or too high to merit meaningful changes in management that result in improved outcomes are not eligible for testing. The benefit of re-stratification should be clearly defined and demonstrated, preferably through real-world randomized clinical utility studies.

Rule-out tests that aim to decrease the pretest probability of malignancy should have a high sensitivity and NPV. As false negative results are unavoidable, it is imperative to demonstrate that they do not result in a significant delay nor advanced stage at diagnosis compared to standard risk stratification measures. Rule-in tests that aim to up-classify patients by increasing pretest probability of malignancy should have a high specificity and PPV. In patients whose risk is up-classified by a given molecular test, it is important to demonstrate a significantly decreased time to diagnosis and avoidance of intervening diagnostic procedures in the setting of malignancy, along with a lack of significant increase in procedures for falsely up-classified patients with benign nodules.

Decision-making in patients with IPNs is complex, and a given molecular biomarker offers a single data point to be considered within the broader clinical context. Additional factors such as surgical risk, patient preference, resource availability and physician experience also play a role in IPN management.

Finally, reference to specific tests in this document does not imply coverage by this contractor. Abstracts and preprints of journal articles were not considered, as they were not fully reviewed peer-reviewed publications at the time of drafting this local coverage determination. This coverage decision will be periodically re-evaluated based on evolving literature and national consensus guidelines.

Proposed Process Information

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Bibliography
    1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022;72(1):7-33. doi:10.3322/caac.21708
    2. Howlader N, Forjaz G, Mooradian MJ, et al. The effect of advances in lung-cancer treatment on population mortality. N Engl J Med. 2020;383(7):640-649. doi:10.1056/NEJMoa1916623
    3. National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365(5):395-409. doi:10.1056/NEJMoa1102873
    4. de Koning HJ, van der Aalst CM, de Jong PA, et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N Engl J Med. 2020;382(6):503-513. doi:10.1056/NEJMoa1911793
    5. National Lung Screening Trial Research Team. Lung cancer incidence and mortality with extended follow-up in the National Lung Screening Trial. J Thorac Oncol. 2019;14(10):1732-1742. doi:10.1016/j.jtho.2019.05.044
    6. US Preventive Services Task Force, Krist AH, Davidson KW, et al. Screening for lung cancer: US Preventive Services Task Force recommendation statement. JAMA. 2021;325(10):962-970. doi:10.1001/jama.2021.1117
    7. Centers for Medicare & Medicaid Services. NCA-Screening for Lung Cancer with Low Dose Computed Tomography (LDCT) (CAG-00439R)-Decision Memo. Accessed July 3, 2023. https://www.cms.gov/medicare-coverage-database/view/ncacal-decision-memo.aspx?proposed=N&ncaid=304.
    8. Mazzone PJ, Silvestri GA, Souter LH, et al. Screening for lung cancer: CHEST guideline and expert panel report. Chest. 2021;160(5):e427-e494. doi:10.1016/j.chest.2021.06.063
    9. NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines). Lung Cancer Screening. Version 1.2023.
    10. Gould MK, Tang T, Liu IL, et al. Recent trends in the identification of incidental pulmonary nodules. Am J Respir Crit Care Med. 2015;192(10):1208-1214. doi:10.1164/rccm.201505-0990OC
    11. Karush J, Arndt A, Shah P, et al. Improved false-positive rates and the overestimation of unintended harm from lung cancer screening. Lung. 2019;197(3):327-332. doi:10.1007/s00408-019-00217-4
    12. Nishi SPE, Zhou J, Okereke I, Kuo YF, Goodwin J. Use of imaging and diagnostic procedures after low-dose CT screening for lung cancer. Chest. 2020;157(2):427-434. doi:10.1016/j.chest.2019.08.2187
    13. Gould MK, Donington J, Lynch WR, et al. Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2013;143(5 Suppl):e93S-e120S. doi:10.1378/chest.12-2351
    14. Paez R, Kammer MN, Massion P. Risk stratification of indeterminate pulmonary nodules. Curr Opin Pulm Med. 2021;27(4):240-248. doi:10.1097/mcp.0000000000000780
    15. MacMahon H, Naidich DP, Goo JM, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology. 2017;284(1):228-243. doi:10.1148/radiol.2017161659
    16. Baldwin DR, Callister ME. The British Thoracic Society guidelines on the investigation and management of pulmonary nodules. Thorax. 2015;70(8):794-798. doi:10.1136/thoraxjnl-2015-207221
    17. American College of Radiology. Lung-RADS® v2022. Accessed June 28, 2023. https://www.acr.org/-/media/ACR/Files/RADS/Lung-RADS/Lung-RADS-2022.pdf.
    18. Swensen SJ, Silverstein MD, Ilstrup DM, Schleck CD, Edell ES. The probability of malignancy in solitary pulmonary nodules. Application to small radiologically indeterminate nodules. Arch Intern Med. 1997;157(8):849-855.
    19. Gould MK, Ananth L, Barnett PG. A clinical model to estimate the pretest probability of lung cancer in patients with solitary pulmonary nodules. Chest. 2007;131(2):383-388. doi:10.1378/chest.06-1261
    20. McWilliams A, Tammemagi MC, Mayo JR, et al. Probability of cancer in pulmonary nodules detected on first screening CT. N Engl J Med. 2013;369(10):910-919. doi:10.1056/NEJMoa1214726
    21. Herder GJ, van Tinteren H, Golding RP, et al. Clinical prediction model to characterize pulmonary nodules: validation and added value of 18F-fluorodeoxyglucose positron emission tomography. Chest. 2005;128(4):2490-2496. doi:10.1378/chest.128.4.2490
    22. Deppen SA, Blume JD, Aldrich MC, et al. Predicting lung cancer prior to surgical resection in patients with lung nodules. J Thorac Oncol. 2014;9(10):1477-1484. doi:10.1097/jto.0000000000000287
    23. Choi HK, Ghobrial M, Mazzone PJ. Models to estimate the probability of malignancy in patients with pulmonary nodules. Ann Am Thorac Soc. 2018;15(10):1117-1126. doi:10.1513/AnnalsATS.201803-173CME
    24. Tanner NT, Porter A, Gould MK, Li XJ, Vachani A, Silvestri GA. Physician assessment of pretest probability of malignancy and adherence with guidelines for pulmonary nodule evaluation. Chest. 2017;152(2):263-270. doi:10.1016/j.chest.2017.01.018
    25. Maiga AW, Deppen SA, Massion PP, et al. Communication about the probability of cancer in indeterminate pulmonary nodules. JAMA Surg. 2018;153(4):353-357. doi:10.1001/jamasurg.2017.4878
    26. Massion PP, Walker RC. Indeterminate pulmonary nodules: risk for having or for developing lung cancer? Cancer Prev Res (Phila). 2014;7(12):1173-1178. doi:10.1158/1940-6207.Capr-14-0364
    27. Wiener RS, Gould MK, Slatore CG, Fincke BG, Schwartz LM, Woloshin S. Resource use and guideline concordance in evaluation of pulmonary nodules for cancer: too much and too little care. JAMA Intern Med. 2014;174(6):871-880. doi:10.1001/jamainternmed.2014.561
    28. McDonald JS, Koo CW, White D, Hartman TE, Bender CE, Sykes AG. Addition of the Fleischner Society Guidelines to chest CT examination interpretive reports improves adherence to recommended follow-up care for incidental pulmonary nodules. Acad Radiol. 2017;24(3):337-344. doi:10.1016/j.acra.2016.08.026
    29. Tukey MH, Wiener RS. Population-based estimates of transbronchial lung biopsy utilization and complications. Respir Med. 2012;106(11):1559-1565. doi:10.1016/j.rmed.2012.08.008
    30. Ernst A, Silvestri GA, Johnstone D. Interventional pulmonary procedures: guidelines from the American College of Chest Physicians. Chest. 2003;123(5):1693-1717. doi:10.1378/chest.123.5.1693
    31. Silvestri GA, Vachani A, Whitney D, et al. A bronchial genomic classifier for the diagnostic evaluation of lung cancer. N Engl J Med. 2015;373(3):243-251. doi:10.1056/NEJMoa1504601
    32. Wen SWC, Wen J, Hansen TF, Jakobsen A, Hilberg O. Cell free methylated tumor DNA in bronchial lavage as an additional tool for diagnosing lung cancer-a systematic review. Cancers (Basel). 2022;14(9):2254. doi:10.3390/cancers14092254
    33. Roncarati R, Lupini L, Miotto E, et al. Molecular testing on bronchial washings for the diagnosis and predictive assessment of lung cancer. Mol Oncol. 2020;14(9):2163-2175. doi:10.1002/1878-0261.12713
    34. Nair VS, Hui AB, Chabon JJ, et al. Genomic profiling of bronchoalveolar lavage fluid in lung cancer. Cancer Res. 2022;82(16):2838-2847. doi:10.1158/0008-5472.Can-22-0554
    35. Wen SWC, Andersen RF, Rasmussen K, et al. Validating methylated HOXA9 in bronchial lavage as a diagnostic tool in patients suspected of lung cancer. Cancers (Basel). 2021;13(16):4223. doi:10.3390/cancers13164223
    36. Paez R, Kammer MN, Tanner NT, et al. Update on biomarkers for the stratification of indeterminate pulmonary nodules.[published online ahead of print, 2023 May 25]. Chest. 2023;S0012-2692(23):00785-00787. doi:10.1016/j.chest.2023.05.025
    37. Spira A, Beane J, Shah V, et al. Effects of cigarette smoke on the human airway epithelial cell transcriptome. Proc Natl Acad Sci U S A. 2004;101(27):10143-10148. doi:10.1073/pnas.0401422101
    38. Spira A, Beane JE, Shah V, et al. Airway epithelial gene expression in the diagnostic evaluation of smokers with suspect lung cancer. Nat Med. Mar 2007;13(3):361-366. doi:10.1038/nm1556
    39. Whitney DH, Elashoff MR, Porta-Smith K, et al. Derivation of a bronchial genomic classifier for lung cancer in a prospective study of patients undergoing diagnostic bronchoscopy. BMC Med Genomics. 2015;8:18. doi:10.1186/s12920-015-0091-3
    40. Hu Z, Whitney D, Anderson JR, et al. Analytical performance of a bronchial genomic classifier. BMC Cancer. 2016;16:161. doi:10.1186/s12885-016-2153-0
    41. Vachani A, Whitney DH, Parsons EC, et al. Clinical utility of a bronchial genomic classifier in patients with suspected lung cancer. Chest. 2016;150(1):210-218. doi:10.1016/j.chest.2016.02.636
    42. 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. 2016;16(1):66. doi:10.1186/s12890-016-0217-1
    43. Lee HJ, Mazzone P, Feller-Kopman D, et al. Impact of the Percepta Genomic Classifier on clinical management decisions in a multicenter prospective study. Chest. 2021;159(1):401-412. doi:10.1016/j.chest.2020.07.067
    44. Choi Y, Qu J, Wu S, et al. Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts. BMC Med Genomics. 2020;13(Suppl 10):151. doi:10.1186/s12920-020-00782-1
    45. Mazzone P, Dotson T, Wahidi MM, et al. Clinical validation and utility of Percepta GSC for the evaluation of lung cancer. PLoS One. 2022;17(7):e0268567.
    46. Johnson MK, Wu S, Pankratz DG, et al. Analytical validation of the Percepta genomic sequencing classifier; an RNA next generation sequencing assay for the assessment of Lung Cancer risk of suspicious pulmonary nodules. BMC Cancer. 2021;21(1):400. doi:10.1186/s12885-021-08130-x
    47. Raval AA, Benn BS, Benzaquen S, et al. Reclassification of risk of malignancy with Percepta Genomic Sequencing Classifier following nondiagnostic bronchoscopy. Respir Med. 2022;204:106990. doi:10.1016/j.rmed.2022.106990
    48. Sethi S, Oh S, Chen A, et al. Percepta Genomic Sequencing Classifier and decision-making in patients with high-risk lung nodules: a decision impact study. BMC Pulm Med. 2022;22(1):26. doi:10.1186/s12890-021-01772-4
    49. Brawley OW, Flenaugh EL. Low-dose spiral CT screening and evaluation of the solitary pulmonary nodule. Oncology (Williston Park). 2014;28(5):441-446.
    50. Kammer MN, Massion PP. Noninvasive biomarkers for lung cancer diagnosis, where do we stand? Journal of thoracic disease. 2020;12(6):3317-3330. doi:10.21037/jtd-2019-ndt-10
    51. Mazzone PJ, Sears CR, Arenberg DA, et al. Evaluating molecular biomarkers for the early detection of lung cancer: when is a biomarker ready for clinical use? An official American Thoracic Society policy statement. Am J Respir Crit Care Med.
    52. Silvestri GA, Bevill BT, Huang J, et al. An evaluation of diagnostic yield from bronchoscopy: the impact of clinical/radiographic factors, procedure type, and degree of suspicion for cancer. Chest. 2020;157(6):1656-1664. doi:10.1016/j.chest.2019.12.024
Open Meetings
Meeting Date Meeting States Meeting Information
11/07/2023 Kentucky

This policy will be presented at the open meeting :

Marshall Women's Health and Education Center
4123 Dutchmans Lan
Plaza III – 1B – Suite 108
Louisville, KY 40207

Please review our website for additional information.

11/08/2023 Ohio

This policy will be presented at the open meeting :

OSU Wexner Medical Center
Biomedical Research Tower
460 W 12th Ave.
Room 105 – 1st Floor
Columbus, Ohio 43210

Please review our website for additional information.

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10/05/2023
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10/05/2023
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Bibliography
    1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022;72(1):7-33. doi:10.3322/caac.21708
    2. Howlader N, Forjaz G, Mooradian MJ, et al. The effect of advances in lung-cancer treatment on population mortality. N Engl J Med. 2020;383(7):640-649. doi:10.1056/NEJMoa1916623
    3. National Lung Screening Trial Research Team, Aberle DR, Adams AM, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365(5):395-409. doi:10.1056/NEJMoa1102873
    4. de Koning HJ, van der Aalst CM, de Jong PA, et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N Engl J Med. 2020;382(6):503-513. doi:10.1056/NEJMoa1911793
    5. National Lung Screening Trial Research Team. Lung cancer incidence and mortality with extended follow-up in the National Lung Screening Trial. J Thorac Oncol. 2019;14(10):1732-1742. doi:10.1016/j.jtho.2019.05.044
    6. US Preventive Services Task Force, Krist AH, Davidson KW, et al. Screening for lung cancer: US Preventive Services Task Force recommendation statement. JAMA. 2021;325(10):962-970. doi:10.1001/jama.2021.1117
    7. Centers for Medicare & Medicaid Services. NCA-Screening for Lung Cancer with Low Dose Computed Tomography (LDCT) (CAG-00439R)-Decision Memo. Accessed July 3, 2023. https://www.cms.gov/medicare-coverage-database/view/ncacal-decision-memo.aspx?proposed=N&ncaid=304.
    8. Mazzone PJ, Silvestri GA, Souter LH, et al. Screening for lung cancer: CHEST guideline and expert panel report. Chest. 2021;160(5):e427-e494. doi:10.1016/j.chest.2021.06.063
    9. NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines). Lung Cancer Screening. Version 1.2023.
    10. Gould MK, Tang T, Liu IL, et al. Recent trends in the identification of incidental pulmonary nodules. Am J Respir Crit Care Med. 2015;192(10):1208-1214. doi:10.1164/rccm.201505-0990OC
    11. Karush J, Arndt A, Shah P, et al. Improved false-positive rates and the overestimation of unintended harm from lung cancer screening. Lung. 2019;197(3):327-332. doi:10.1007/s00408-019-00217-4
    12. Nishi SPE, Zhou J, Okereke I, Kuo YF, Goodwin J. Use of imaging and diagnostic procedures after low-dose CT screening for lung cancer. Chest. 2020;157(2):427-434. doi:10.1016/j.chest.2019.08.2187
    13. Gould MK, Donington J, Lynch WR, et al. Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2013;143(5 Suppl):e93S-e120S. doi:10.1378/chest.12-2351
    14. Paez R, Kammer MN, Massion P. Risk stratification of indeterminate pulmonary nodules. Curr Opin Pulm Med. 2021;27(4):240-248. doi:10.1097/mcp.0000000000000780
    15. MacMahon H, Naidich DP, Goo JM, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017. Radiology. 2017;284(1):228-243. doi:10.1148/radiol.2017161659
    16. Baldwin DR, Callister ME. The British Thoracic Society guidelines on the investigation and management of pulmonary nodules. Thorax. 2015;70(8):794-798. doi:10.1136/thoraxjnl-2015-207221
    17. American College of Radiology. Lung-RADS® v2022. Accessed June 28, 2023. https://www.acr.org/-/media/ACR/Files/RADS/Lung-RADS/Lung-RADS-2022.pdf.
    18. Swensen SJ, Silverstein MD, Ilstrup DM, Schleck CD, Edell ES. The probability of malignancy in solitary pulmonary nodules. Application to small radiologically indeterminate nodules. Arch Intern Med. 1997;157(8):849-855.
    19. Gould MK, Ananth L, Barnett PG. A clinical model to estimate the pretest probability of lung cancer in patients with solitary pulmonary nodules. Chest. 2007;131(2):383-388. doi:10.1378/chest.06-1261
    20. McWilliams A, Tammemagi MC, Mayo JR, et al. Probability of cancer in pulmonary nodules detected on first screening CT. N Engl J Med. 2013;369(10):910-919. doi:10.1056/NEJMoa1214726
    21. Herder GJ, van Tinteren H, Golding RP, et al. Clinical prediction model to characterize pulmonary nodules: validation and added value of 18F-fluorodeoxyglucose positron emission tomography. Chest. 2005;128(4):2490-2496. doi:10.1378/chest.128.4.2490
    22. Deppen SA, Blume JD, Aldrich MC, et al. Predicting lung cancer prior to surgical resection in patients with lung nodules. J Thorac Oncol. 2014;9(10):1477-1484. doi:10.1097/jto.0000000000000287
    23. Choi HK, Ghobrial M, Mazzone PJ. Models to estimate the probability of malignancy in patients with pulmonary nodules. Ann Am Thorac Soc. 2018;15(10):1117-1126. doi:10.1513/AnnalsATS.201803-173CME
    24. Tanner NT, Porter A, Gould MK, Li XJ, Vachani A, Silvestri GA. Physician assessment of pretest probability of malignancy and adherence with guidelines for pulmonary nodule evaluation. Chest. 2017;152(2):263-270. doi:10.1016/j.chest.2017.01.018
    25. Maiga AW, Deppen SA, Massion PP, et al. Communication about the probability of cancer in indeterminate pulmonary nodules. JAMA Surg. 2018;153(4):353-357. doi:10.1001/jamasurg.2017.4878
    26. Massion PP, Walker RC. Indeterminate pulmonary nodules: risk for having or for developing lung cancer? Cancer Prev Res (Phila). 2014;7(12):1173-1178. doi:10.1158/1940-6207.Capr-14-0364
    27. Wiener RS, Gould MK, Slatore CG, Fincke BG, Schwartz LM, Woloshin S. Resource use and guideline concordance in evaluation of pulmonary nodules for cancer: too much and too little care. JAMA Intern Med. 2014;174(6):871-880. doi:10.1001/jamainternmed.2014.561
    28. McDonald JS, Koo CW, White D, Hartman TE, Bender CE, Sykes AG. Addition of the Fleischner Society Guidelines to chest CT examination interpretive reports improves adherence to recommended follow-up care for incidental pulmonary nodules. Acad Radiol. 2017;24(3):337-344. doi:10.1016/j.acra.2016.08.026
    29. Tukey MH, Wiener RS. Population-based estimates of transbronchial lung biopsy utilization and complications. Respir Med. 2012;106(11):1559-1565. doi:10.1016/j.rmed.2012.08.008
    30. Ernst A, Silvestri GA, Johnstone D. Interventional pulmonary procedures: guidelines from the American College of Chest Physicians. Chest. 2003;123(5):1693-1717. doi:10.1378/chest.123.5.1693
    31. Silvestri GA, Vachani A, Whitney D, et al. A bronchial genomic classifier for the diagnostic evaluation of lung cancer. N Engl J Med. 2015;373(3):243-251. doi:10.1056/NEJMoa1504601
    32. Wen SWC, Wen J, Hansen TF, Jakobsen A, Hilberg O. Cell free methylated tumor DNA in bronchial lavage as an additional tool for diagnosing lung cancer-a systematic review. Cancers (Basel). 2022;14(9):2254. doi:10.3390/cancers14092254
    33. Roncarati R, Lupini L, Miotto E, et al. Molecular testing on bronchial washings for the diagnosis and predictive assessment of lung cancer. Mol Oncol. 2020;14(9):2163-2175. doi:10.1002/1878-0261.12713
    34. Nair VS, Hui AB, Chabon JJ, et al. Genomic profiling of bronchoalveolar lavage fluid in lung cancer. Cancer Res. 2022;82(16):2838-2847. doi:10.1158/0008-5472.Can-22-0554
    35. Wen SWC, Andersen RF, Rasmussen K, et al. Validating methylated HOXA9 in bronchial lavage as a diagnostic tool in patients suspected of lung cancer. Cancers (Basel). 2021;13(16):4223. doi:10.3390/cancers13164223
    36. Paez R, Kammer MN, Tanner NT, et al. Update on biomarkers for the stratification of indeterminate pulmonary nodules.[published online ahead of print, 2023 May 25]. Chest. 2023;S0012-2692(23):00785-00787. doi:10.1016/j.chest.2023.05.025
    37. Spira A, Beane J, Shah V, et al. Effects of cigarette smoke on the human airway epithelial cell transcriptome. Proc Natl Acad Sci U S A. 2004;101(27):10143-10148. doi:10.1073/pnas.0401422101
    38. Spira A, Beane JE, Shah V, et al. Airway epithelial gene expression in the diagnostic evaluation of smokers with suspect lung cancer. Nat Med. Mar 2007;13(3):361-366. doi:10.1038/nm1556
    39. Whitney DH, Elashoff MR, Porta-Smith K, et al. Derivation of a bronchial genomic classifier for lung cancer in a prospective study of patients undergoing diagnostic bronchoscopy. BMC Med Genomics. 2015;8:18. doi:10.1186/s12920-015-0091-3
    40. Hu Z, Whitney D, Anderson JR, et al. Analytical performance of a bronchial genomic classifier. BMC Cancer. 2016;16:161. doi:10.1186/s12885-016-2153-0
    41. Vachani A, Whitney DH, Parsons EC, et al. Clinical utility of a bronchial genomic classifier in patients with suspected lung cancer. Chest. 2016;150(1):210-218. doi:10.1016/j.chest.2016.02.636
    42. 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. 2016;16(1):66. doi:10.1186/s12890-016-0217-1
    43. Lee HJ, Mazzone P, Feller-Kopman D, et al. Impact of the Percepta Genomic Classifier on clinical management decisions in a multicenter prospective study. Chest. 2021;159(1):401-412. doi:10.1016/j.chest.2020.07.067
    44. Choi Y, Qu J, Wu S, et al. Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts. BMC Med Genomics. 2020;13(Suppl 10):151. doi:10.1186/s12920-020-00782-1
    45. Mazzone P, Dotson T, Wahidi MM, et al. Clinical validation and utility of Percepta GSC for the evaluation of lung cancer. PLoS One. 2022;17(7):e0268567.
    46. Johnson MK, Wu S, Pankratz DG, et al. Analytical validation of the Percepta genomic sequencing classifier; an RNA next generation sequencing assay for the assessment of Lung Cancer risk of suspicious pulmonary nodules. BMC Cancer. 2021;21(1):400. doi:10.1186/s12885-021-08130-x
    47. Raval AA, Benn BS, Benzaquen S, et al. Reclassification of risk of malignancy with Percepta Genomic Sequencing Classifier following nondiagnostic bronchoscopy. Respir Med. 2022;204:106990. doi:10.1016/j.rmed.2022.106990
    48. Sethi S, Oh S, Chen A, et al. Percepta Genomic Sequencing Classifier and decision-making in patients with high-risk lung nodules: a decision impact study. BMC Pulm Med. 2022;22(1):26. doi:10.1186/s12890-021-01772-4
    49. Brawley OW, Flenaugh EL. Low-dose spiral CT screening and evaluation of the solitary pulmonary nodule. Oncology (Williston Park). 2014;28(5):441-446.
    50. Kammer MN, Massion PP. Noninvasive biomarkers for lung cancer diagnosis, where do we stand? Journal of thoracic disease. 2020;12(6):3317-3330. doi:10.21037/jtd-2019-ndt-10
    51. Mazzone PJ, Sears CR, Arenberg DA, et al. Evaluating molecular biomarkers for the early detection of lung cancer: when is a biomarker ready for clinical use? An official American Thoracic Society policy statement. Am J Respir Crit Care Med.
    52. Silvestri GA, Bevill BT, Huang J, et al. An evaluation of diagnostic yield from bronchoscopy: the impact of clinical/radiographic factors, procedure type, and degree of suspicion for cancer. Chest. 2020;157(6):1656-1664. doi:10.1016/j.chest.2019.12.024

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Keywords

  • Indeterminate Pulmonary Nodules
  • Molecular Biomarkers for Risk Stratification

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