Molecular Testing of Pulmonary Specimens - CAM 326

Pulmonary nodules are well-defined lesions found in lung tissue. These nodules are found on cross-sectional imaging and are frequently “incidental” (i.e. found on imaging not originally performed to identify the nodules). Assessment of malignancy risk is critical to managing these nodules, and a variety of tests have been used to accurately evaluate them. Some of these tests use cells obtained from bronchoscopies; these cells are purported to contain molecular markers indicative of malignancy. Evaluation of these cells has been used to determine malignancy risk of these nodules.

Pulmonary nodules are a common clinical problem that may be found incidentally on a chest x-ray or computed tomography (CT) scan or during lung cancer screening studies of smokers. The primary question after the detection of a pulmonary nodule is the probability of malignancy, with subsequent management of the nodule based on various factors such as the radiographic characteristics of the nodules (e.g., size, shape, density) and patient factors (e.g., age, smoking history, previous cancer history, family history, environmental/occupational exposures). The key challenge in the diagnostic workup for pulmonary nodules is appropriately ruling in patients for invasive diagnostic procedures and ruling out patients who should forgo invasive diagnostic procedures. However, due to the low positive predictive value of pulmonary nodules detected radiographically, many unnecessary invasive diagnostic procedures and/or surgeries are performed to confirm or eliminate the diagnosis of lung cancer.

Proteomics is the study of the structure and function of proteins. The study of the concentration, structure and other characteristics of proteins in various bodily tissues, fluids, and other materials has been proposed as a method to identify and manage various diseases, including cancer. In proteomics, multiple test methods are used to study proteins. Immunoassays use antibodies to detect the concentration and/or structure of proteins. Mass spectrometry is an analytic technique that ionizes proteins into smaller fragments and determines mass and composition to identify and characterize them.

Plasma-Based Proteomic Screening for Pulmonary Nodules
Plasma-based proteomic screening has been investigated to risk-stratify pulmonary nodules as likely benign to increase the number of patients who undergo serial CT scans of their nodules (active surveillance), instead of invasive procedures such as CT-guided biopsy or surgery. Additionally, proteomic testing may also determine a likely malignancy in clinically low-risk or intermediate-risk pulmonary nodules, thereby permitting earlier detection in a subset of patients.

Xpresys Lung is a plasma-based proteomic screening test that measures the relative abundance of proteins from multiple disease pathways associated with lung cancer using an analytic technique called multiple reaction monitoring mass spectroscopy. The role of the test is to aid physicians in differentiating likely benign versus likely malignant nodules. If the test yields a likely benign result, patients may choose active surveillance via serial CT scans to monitor the pulmonary nodule. If the test yields a likely malignant result, invasive diagnostic procedures would be indicated. The test is therefore only used in the management of pulmonary nodules to rule in or out invasive diagnostic procedures and does not diagnose lung cancer.

Gene expression profiling is the measurement of the activity of genes with cells. Messenger RNA (mRNA) serves at the bridge between DNA and functional proteins. Multiple molecular techniques such as Northern blots, ribonuclease protection assay, in situ hybridization, spotted complementary DNA arrays, oligonucleotide arrays, reverse transcriptase polymerase chain reaction, and transcriptome sequencing are used in gene expression profiling. An important role of gene expression profiling in molecular diagnostics is to detect cancer-associated gene expression of clinical samples to assess for the risk for malignancy.

Gene Expression Profiling for an Indeterminate Bronchoscopy Result
The Percepta Bronchial Genomic Classifier is a 23-gene gene expression profiling test that analyzes genomic changes in the airways of current or former smoker s to assess a patient’s risk of having lung cancer, without the direct testing of a pulmonary nodule. The test is indicated for current and former smokes following an indeterminate bronchoscopy result to determine subsequent management of pulmonary nodules (e.g., active surveillance or invasive diagnostic procedures), and does not diagnose lung cancer. 

These tests are considered laboratory developed tests (LDT); developed, validated and performed by individual laboratories. LDTs are regulated by the Centers for Medicare and Medicaid (CMS) as high-complexity tests under the Clinical Laboratory Improvement Amendments of 1988 (CLIA’88). As an LDT, the U. S. Food and Drug Administration has not approved or cleared this test; however, FDA clearance or approval is not currently required for clinical use.


  1. The use of gene expression profiling on bronchial brushings (e.g., including but not limited to Percepta Bronchial Genomic Classifier) is considered NOT MEDICALLY NECESSARY for all indications, including in patients with indeterminate bronchoscopy results from undiagnosed pulmonary nodules.
  2. The use of genomic testing to improve the diagnosis of idiopathic pulmonary fibrosis (e.g., including but not limited to, Envisia Genomic Classifier) is considered NOT MEDICALLY NECESSARY for all indications.

In the United States, over 1.5 million lung nodules are detected annually (Kearney et al., 2017). These pulmonary nodules may arise due to a variety of conditions, some malignant (i.e. cancer), some benign (such as an infection). Since treatment varies widely between malignant and benign nodules, it is crucial to have well-validated and accurate methods to assess risk of malignancy. Traditionally, malignancy has been evaluated using a combination of factors, such as clinical, histological, and radiographic features. Once an initial assessment of malignancy has been performed, further management such as computed tomography (CT) surveillance or biopsy may follow. Low-dose computed tomography (LDCT) is the current standard for lung cancer screening. However, a limitation of the screening is that LDCTshows indeterminate pulmonary nodules which are not clearly defined as benign or cancerous. Assessment of a malignant nodule typically involves expensive biopsies whereas benign nodules may be only placed under close surveillance. Clinicians must often weigh the risk of a missed malignant diagnosis against performing an invasive procedure that may ultimately be unnecessary.

To address this population of indeterminate pulmonary nodules, some proprietary tests have been developed, such as Veracyte’s Bronchial Genomic Classifier (Percepta). This test focuses on molecular analysis of the nodules, rather than clinical or radiographic analysis. The Percepta Bronchial Genomic Classifier uses cells collected during bronchoscopy to detect genomic changes indicative of a cancerous nodule. Percepta “is designed to reduce the number of invasive biopsies and other procedures that can follow when suspicious lung nodules are found on computerized tomography (CT) scans”. Percepta purports that it can add diagnostic value without an invasive biopsy.

Another condition that may cause these pulmonary nodules is idiopathic pulmonary fibrosis (IPF). Although the cause is unknown by definition, clinical management of this condition may involve assessment of these nodules and further biopsy. Evaluation of these nodules includes several of the same procedures discussed above, such as clinical assessment, imaging, and pulmonary function tests. Diagnosis of IPF typically requires “exclusion of other known causes of interstitial lung disease (ILD) and either definite features of usual interstitial pneumonia (UIP) on high resolution computed tomography (HRCT) or certain combinations of HRCT and histopathologic features of UIP”. Much debate exists around the role of the lung biopsy in diagnosis of IPF; authorities are conflicted on its importance in IPF assessment.

Veracyte has developed a genomic test named Envisia intended to aid physicians in differentiating between “idiopathic pulmonary fibrosis (IPF) and other interstitial lung diseases (ILD), without having to do a surgical lung biopsy”. Envisia uses tissue samples obtained from a transbronchial biopsy and evaluates RNA of 190 genes purported to have common associations with fibrosis and inflammation. The results then report either “positive” or “negative” for usual interstitial pneumonia, considered to be the signature histopathologic pattern for IPF.

Percepta is currently the only molecular test available for the assessment of pulmonary nodules that uses gene expression profiling. There are plasma-based proteomic tests that can be used to screen pulmonary nodules and estimate their risk of malignancy. Nodify XL2™ (previously called Xpresys Lung®, Xpresys Lung 2®, and BDX-XL2) is a plasma-based proteomic screening test that measures the abundance of proteins known to be related to lung cancer. Nodify XL2™ is reported to have a 90% negative predictive value. REVEAL Lung Nodule Characterization is a proteomic test for classification of pulmonary nodules in current smokers that calculates a risk score between 0 and 100 based on three clinical factors (smoking history, patient age, nodule size) and three blood proteins. REVEAL Lung Nodule Characterization is reported to have a sensitivity of 94% and a negative predictive value of 94%. Lung Cancer Detector Test (LCDT1) is a proteomic test being developed for stage 1 non-small cell lung cancer detection. LCDT1 is expected to have 95.6% accuracy, 89.1% sensitivity, and 97.7% specificity. EarlyCDT-Lung is a serum-based test that measures seven autoantibodies associated with lung cancer to estimate the risk of malignancy in small cell lung cancer and non-small cell lung cancer. EarlyCDT-Lung is reported to have 41% sensitivity and 87% specificity.

Analytical Validity
Hu et al. (2016) conducted studies to evaluate analytical performance of gene expression profiling test (Percepta test) using bronchial brushing specimens. The authors found that “analytical sensitivity studies demonstrated tolerance to variation in RNA input (157 ng to 243 ng). Analytical specificity studies utilizing cancer positive and cancer negative samples mixed with either blood (up to 10 % input mass) or genomic DNA (up to 10 % input mass) demonstrated no assay interference.” The authors concluded that “analytical sensitivity, analytical specificity and robustness of the Percepta test were successfully verified, supporting its suitability for clinical use”.

Pankratz et al. (2017) aimed to develop a genomic classifier to distinguish usual interstitial pneumonia (UIP) from non-UIP in tissue samples obtained by transbronchial biopsy (TBB). The authors stated that this study was performed because UIP was the hallmark symptom of idiopathic pulmonary fibrosis (IPF) and imaging to identify UIP was frequently inconclusive. 283 samples from TBB were taken from 84 subjects, and “exome-enriched RNA sequencing” was performed on these samples. Then, a machine learning algorithm was created from 53 of these samples. This algorithm was then validated in the remaining 31 samples. The authors found that this algorithm distinguished UIP from non-UIP conditions with an area under curve (AUC) of 0.86 with a single sample. The sensitivity was found to be 63%, and the specificity was found to be 86%. The AUC improved to 0.92 when 3-5 TBB samples were included. The authors concluded that “genomic analysis and machine learning improves the utility of TBB for the diagnosis of UIP”, but acknowledged that “this approach requires validation in an independent cohort of subjects before application in the clinic”.

Roncarati et al. (2020) evaluated the suitability of molecular testing for lung cancer assessment on bronchial washings. A novel droplet digital methylation-specific PCR (ddMSP) test was run on bronchial washings taken during fiber-optic bronchoscopy from 91 lung cancer patients and 31 control patients. The ddMSP assessed the aberrant methylenation status of four genes that “display aberrant methylation in more than 50% of cancer samples and no aberrant methylation in normal tissue.” The ddMSP had a 97% sensitivity rate and 74% specificity. Additionally, DNA and RNA analysis of bronchial washings taken from 73 cancer patients and 14 noncancer patients found commonalities among mutations. The authors state that there is predictive value in mutation analysis but “frequent mutation detection in noncancer patients revealed the low specificity of this approach for diagnostic purposes.” The authors concluded that molecular testing on bronchial washings “could be performed to support and complete the current clinical diagnostic/predictive strategies”.

Johnson et al. (2020) analyzed the performance of the Percepta Genomic Sequencing Classifier (GSC) in realistic conditions. Bronchial brushing samples were tested from bronchoscopy of patients with “suspicious lung nodules.” The authors found no significant difference in Percepta GSC results with varying amounts of RNA input, 10% DNA contamination, and up to 11% blood RNA contamination. Additionally, results were reproducible between runs, within runs, and between laboratories. The authors concluded that “the analytical sensitivity, analytical specificity, and reproducibility of Percepta GSC laboratory results were successfully demonstrated under conditions of expected day to day variation in testing. Percepta GSC test results are analytically robust and suitable for routine clinical use”.

Clinical Validity and Utility
Whitney et al. (2015) collected bronchial epithelial cells of 223 cancer-positive and 76 cancer-free subjects undergoing bronchoscopy for suspected lung cancer in a prospective, multi-center study. RNA from these samples was run on gene expression microarrays for training a gene-expression classifier. Out of the 232 genes whose expression levels in the bronchial airway were found to be associated with lung cancer, the authors built a classifier based on the combination of 17 cancer genes, gene expression predictors of smoking status, smoking history, and gender, plus patient age. The authors concluded that their gene classifier “is able to detect lung cancer in current and former smokers who have undergone bronchoscopy for suspicion of lung cancer. Due to the high NPV of the classifier, it could potentially inform clinical decisions regarding the need for further invasive testing in patients whose bronchoscopy is non-diagnostic”.

Silvestri et al. (2015) reported on the diagnostic performance of a gene-expression classifier. 639 current or former smokers undergoing bronchoscopy for suspected lung cancer enrolled in two multicenter prospective studies (AEGIS-1 and AEGIS-2) were evaluated. A gene-expression classifier was measured in epithelial cells to assess the probability of lung cancer. In AEGIS-1, the classifier had a sensitivity of 88% and a specificity of 47%. In AEGIS-2, the classifier had a sensitivity of 89% and a specificity of 47%. The combination of the classifier plus bronchoscopy had a sensitivity of 96% in AEGIS-1 and 98% in AEGIS-2. The authors concluded that “the gene-expression classifier improved the diagnostic performance of bronchoscopy for the detection of lung cancer. In intermediate-risk patients with a nondiagnostic bronchoscopic examination, a negative classifier score provides support for a more conservative diagnostic approach”.

Ferguson et al. (2016) conducted a randomized, prospective decision impact survey study to evaluate pulmonologist recommendations in patients undergoing workup for lung cancer who had an inconclusive bronchoscopy. The authors’ goal was to examine if a negative genomic classifier result that down-classifies a patient from intermediate risk to low risk (<10%) for lung cancer would reduce the rate that physicians recommend more invasive testing among patients with an inconclusive bronchoscopy. The authors found that “invasive procedure recommendations were reduced from 57% without the classifier result to 18% with a negative (low risk) classifier result. Invasive procedure recommendations increased from 50 to 65% with a positive (intermediate risk) classifier result.” The authors concluded that their results “support the potential clinical utility of the classifier to improve management of patients undergoing bronchoscopy for suspect lung cancer by reducing additional invasive procedures in the setting of benign disease”.

Lee et al. (2017) published interim results from a large prospective registry of 665 patients undergoing diagnostic bronchoscopy. In a subset of 209 patients with an intermediate pretest risk of malignancy, Advanced bronchoscopic techniques were used in in 68% of cases. The BGC test results reclassified 74 patients as low risk. At 10 months post follow up the patients reclassified as low risk had a 40% relative reduction in the use of invasive procedures. The authors concluded that the BGC improves the sensitivity of diagnostic bronchoscopy for patients undergoing evaluation for lung cancer and can reduce the number if unnecessary invasive procedures.

Feller-Kopman et al. (2017) assessed the cost effectiveness of bronchoscopy plus a genomic classifier versus bronchoscopy alone in the diagnostic work-up of patients at intermediate risk for lung cancer. They found that “Use of the genomic classifier reduced invasive procedures by 28% at 1 month and 18% at 2 years, respectively. Total costs and QALY gain were similar with classifier use ($27,221 versus $27,183 and 1.512 versus 1.509, respectively), resulting in an incremental cost-effectiveness ratio of $15,052 per QALY”. The authors concluded that use of a genomic classifier was associated with meaningful cost reduction in invasive procedures.

Raghu et al. (2019) evaluated the prospective findings for the clinical validity and utility of a machine-learning based molecular test (Envisia). Findings from 90 patients were used to train the classifier, and then the authors attempted to validate the classifier in a set of 49 patients. The authors found that the classifier identified “usual interstitial pneumonia in transbronchial lung biopsy samples” in these 49 patients at 70% sensitivity and 88% specificity. 42 patients were noted to show “possible or inconsistent usual interstitial pneumonia on HRCT”, and the classifier identified “underlying biopsy-proven usual interstitial pneumonia” at 81% positive predictive value. Clinical diagnoses based on histopathology data agreed with diagnoses based on classifier results at an 86% rate. The authors also found that diagnostic confidence was improved with addition of classifier results in 18 cases of idiopathic pulmonary fibrosis and all 48 patients with “non-diagnostic pathology or non-classifiable fibrosis histopathology” (63% vs 42%). The authors concluded that “The molecular test provided an objective method to aid clinicians and multidisciplinary teams in ascertaining a diagnosis of IPF, particularly for patients without a clear radiological diagnosis in samples that can be obtained by a less invasive method”, noting that further studies were planned.

D’Andrea et al. (2020) evaluated the cost-effectiveness of introducing a bronchial gene-expression classifier (BGC) to “improve the performance of bronchoscopy and the overall diagnostic process for early detection of lung cancer”. The authors evaluated a cohort of former and current smokers with indeterminate pulmonary nodules and compared two different strategies: “(i) location-based strategy—integrated the BGC to the bronchoscopy indication; (ii) simplified strategy—extended use of bronchoscopy plus BGC also on small and peripheral lesions”. The authors modeled the following outcomes: “rate of invasive procedures, quality adjusted-life-years (QALYs), costs and incremental cost-effectiveness ratios”. Both strategies were compared to the standard practice (defined as “bronchoscopy, transthoracic needle aspiration or biopsy (TTNA/B) or surgery, consistent with the current recommendations”). The location-based strategy reduced absolute rate of invasive procedures by 3.3% without increasing costs and resulted in savings when the classifier price was less than $3,000. The simplified strategy reduced the absolute rate of invasive procedures by 10% and created an incremental cost-effectiveness ratio of $10,109 per QALY. The authors concluded that both strategies reduced “unnecessary invasive procedures at high risk of adverse events” and that “the simplified use of BGC for central and peripheral lesions resulted in larger QALYs gains at acceptable cost”. Finally, the authors noted that the location-based strategy is cost-saving if the classifier price declines.

Lee et al. (2021) assessed the impact that Percepta results has on clinical management decisions. The authors conducted a prospective study on 283 patients with low- and intermediate-risk lung nodules across 35 centers in the US. In 35% of cases with a negative Percepta result, the risk of malignancy was down-classified. 79% of the down-classified cases changed their management plan to avoid an invasive procedure. Percepta down-classification did not significantly delay the time to diagnosis for patients with confirmed lung cancer. The authors concluded that “down-classification of nodule malignancy risk with the Percepta test decreased additional invasive procedures without a delay in time to diagnosis among those with lung cancer”. 

Babiarz et al. (2021) tested the use of Percepta Genomic Atlas for identifying key molecular markers in surgical lung biopsy (SLB) specimens, transbronchial needle aspirates (TBNA), and bronchial brush specimens from an initial bronchoscopy at the time of diagnosis. DNA and RNA was extracted from Stage I, Stage II, and Stage III lung cancer SLB tissue. “Genomic alterations were observed in 65% of Stage I, 64% of Stage II and 73% of Stage III samples.” TBNA and bronchial brush specimens were taken from 25 patients; multiple molecular alterations were detected in all patients. The authors concluded that “Percepta Genomic Atlas detects clinically actionable alterations in both SLB of early stage lung cancer tumors and in specimens collected at the time of diagnostic bronchoscopy or needle aspiration prior to surgery”.

American College of Chest Physicians (ACCP)
In 2013, the ACCP published evidence-based clinical practice guidelines for diagnosis and management of lung cancer. The guidelines did not mention gene expression profiling as a potential diagnostic or screening tool.

In 2018, the ACCP published guidelines for screening of lung cancer. In it, the ACCP comments that “Despite their potential promise, evidence that using such biomarkers would improve the efficiency of lung cancer screening is lacking. No applicable studies comparing molecular biomarkers vs NLST or USPSTF criteria were found that could be included in the systematic review for this guideline”.

In 2021, the ACCP updated the guidelines for screening of lung cancer but did not change the recommendations on the use of biomarkers in lung cancer screening (P. J. Mazzone et al., 2021).

National Comprehensive Cancer Network
The NCCN guidelines v5.2021 for Non-Small Cell Lung Cancer did not mention gene expression profiling as a potential diagnostic or screening tool.

The NCCN Guidelines v1.2022 for Small Cell Lung Cancer did not mention gene expression profiling as a potential diagnostic or screening tool.

The NCCN Guidelines v1.2021 for Lung Cancer Screening did not mention gene expression profiling as a potential diagnostic or screening tool.

European Society for Medical Oncology (ESMO)
ESMO does not make any mention of gene expression profiling in its guideline for assessment of lung nodules.

The EMSO Guidelines for metastatic non-small cell lung cancer recommends therapy-predictive biomarker testing after morphological diagnosis. Biomarker testing includes testing for EGFR mutation, ALK rearrangement, ROS1 rearrangement, BRAF mutation, and PD-l1 expression. The guideline states that “this practice will be driven by the availability of treatments and will vary widely between different geopolitical health systems”.

American Thoracic Society, European Respiratory Society, Japanese Respiratory Society, and Latin American Thoracic Society (ATS/ERS/JRS/ALAT)
This set of joint guidelines remarks that “Machine learning using molecular signatures is being developed to make a molecular diagnosis of UIP [usual interstitial pneumonia] in TBBx [transbronchial lung cryobiopsy] specimens but is not yet available in routine clinical practice. The guideline panel acknowledges that recent studies about the utility of molecular diagnostic tools that involve machine learning using TBBx samples are promising”. The guidelines also note that further validation studies are pending.

European Paediatric Soft Tissue Sarcoma Study Group
This study group published a report on the clinical significance of indeterminate pulmonary nodules in rhabdomyosarcoma. The group included 316 patients with non-metastatic rhabdomyosarcoma, 67 of which had indeterminate pulmonary nodules, 249 of which didn’t have nodules. The authors found event-free survival and overall survival rates to be 77% and 82% respectively for patients with indeterminate nodules, and 73.2% and 80.8% respectively for patients without nodules. The authors concluded that their study “demonstrated that indeterminate pulmonary nodules at diagnosis do not affect outcome in patients with otherwise localized RMS. There is no need to biopsy or upstage patients with RMS who have indeterminate pulmonary nodules at diagnosis”.

Fleischner Society White Paper, Diagnostic Criteria for Idiopathic Pulmonary Fibrosis
This guideline focused on diagnostic criteria for IPF, including discussion on traditional features such as clinical, histopathological, and imaging factors. Under the “Areas of uncertainty” subheading, the Society comments that “we anticipate that molecular diagnosis with machine learning will play an increasing role in the diagnosis of IPF, particularly when integrated with clinical and imaging features” and emphasizes the importance of identifying molecular predictors of IPF. 


  1. Arfoosh, R., Nguyen, K., Fish, A. L., Wells, V., Rebeor, D., Seger, A., . . . Torricelli, R. (2019). Risk assessment of indeterminate lung nodule characterization by a novel plasma-protein multiplexed assay in current smokers: Results of a clinical experience program. Biomed Res, 3, 1-4.
  2. Babiarz, J., Hao, Y., Cao, M., Griscom, B., Wilson, D. S., Krimsky, W., . . . Wahidi, M. M. (2021). Detection of actionable molecular alterations through combined DNA/RNA molecular profiling of biopsies collected in early-stage lung cancer at time of diagnosis. In: Wolters Kluwer Health.
  3. BU. (2015, 04/30/2015). Veracyte Initiates Launch of Percepta™ Bronchial Genomic Classifier. Retrieved from
  4. D'Andrea, E., Choudhry, N. K., Raby, B., Weinhouse, G. L., & Najafzadeh, M. (2020). A bronchial-airway gene-expression classifier to improve the diagnosis of lung cancer: Clinical outcomes and cost-effectiveness analysis. Int J Cancer, 146(3), 781-790. doi:10.1002/ijc.32333
  5. Detterbeck, F. C., Lewis, S. Z., Diekemper, R., Addrizzo-Harris, D., & Alberts, W. M. (2013). Executive Summary: Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. CHEST, 143(5 Suppl), 7s-37s. doi:10.1378/chest.12-2377
  6. Feller-Kopman, D., Liu, S., Geisler, B. P., DeCamp, M. M., & Pietzsch, J. B. (2017). Cost-Effectiveness of a Bronchial Genomic Classifier for the Diagnostic Evaluation of Lung Cancer. J Thorac Oncol, 12(8), 1223-1232. doi:10.1016/j.jtho.2017.04.030
  7. Ferguson, J. S., Van Wert, R., Choi, Y., Rosenbluth, M. J., Smith, K. P., Huang, J., & Spira, A. (2016). Impact of a bronchial genomic classifier on clinical decision making in patients undergoing diagnostic evaluation for lung cancer. BMC Pulm Med, 16(1), 66. doi:10.1186/s12890-016-0217-1
  8. Goebel, C., Louden, C. L., McKenna, R., Jr., Onugha, O., Wachtel, A., & Long, T. (2020). Blood test shows high accuracy in detecting stage I non-small cell lung cancer. BMC Cancer, 20(1), 137. doi:10.1186/s12885-020-6625-x
  9. Hu, Z., Whitney, D., Anderson, J. R., Cao, M., Ho, C., Choi, Y., . . . Walsh, P. S. (2016). Analytical performance of a bronchial genomic classifier. BMC Cancer, 16, 161. doi:10.1186/s12885-016-2153-0
  10. Islam, S. (2018). Flexible bronchoscopy in adults: Associated diagnostic and therapeutic procedures. Retrieved from
  11. Johnson, M. K., Wu, S., Pankratz, D. G., Fedorowicz, G., Anderson, J., Ding, J., . . . Huang, J. (2021). 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, 21(1), 400. doi:10.1186/s12885-021-08130-x
  12. Kearney, P., Hunsucker, S. W., Li, X. J., Porter, A., Springmeyer, S., & Mazzone, P. (2017). An integrated risk predictor for pulmonary nodules. PLoS One, 12(5), e0177635. doi:10.1371/journal.pone.0177635
  13. King, T. (2019). Clinical manifestations and diagnosis of idiopathic pulmonary fibrosis. Retrieved from
  14. Lee, H., Whitten, P., Bhadra, K., Dotson, T., Hogarth, D., Benzaquen, S., . . . Spira, A. (2017). Prospective Utility of a Bronchial Genomic Classifier for Lung Cancer Detection: Interim Results From a Multicenter Prospective Registry. CHEST, 152(4). doi:10.1016/j.chest.2017.08.658
  15. Lee, H. J., Mazzone, P., Feller-Kopman, D., Yarmus, L., Hogarth, K., Lofaro, L. R., . . . Wahidi, M. M. (2021). Impact of the Percepta Genomic Classifier on Clinical Management Decisions in a Multicenter Prospective Study. Chest, 159(1), 401-412. doi:10.1016/j.chest.2020.07.067
  16. Lynch, D. A., Sverzellati, N., Travis, W. D., Brown, K. K., Colby, T. V., Galvin, J. R., . . . Wells, A. U. (2018). Diagnostic criteria for idiopathic pulmonary fibrosis: a Fleischner Society White Paper. Lancet Respir Med, 6(2), 138-153. doi:10.1016/s2213-2600(17)30433-2
  17. Mazzone, P. J., Silvestri, G. A., Patel, S., Kanne, J. P., Kinsinger, L. S., Wiener, R. S., . . . Detterbeck, F. C. (2018). Screening for Lung Cancer: CHEST Guideline and Expert Panel Report. CHEST, 153(4), 954-985. doi:10.1016/j.chest.2018.01.016
  18. Mazzone, P. J., Silvestri, G. A., Souter, L. H., Caverly, T. J., Kanne, J. P., Katki, H. A., . . . Detterbeck, F. C. (2021). Screening for Lung Cancer: CHEST Guideline and Expert Panel Report. Chest. doi:10.1016/j.chest.2021.06.063
  19. NCCN. (2019). Lung Cancer Screening, Version 1. Retrieved from
  20. NCCN. (2020a). Lung Cancer Screening, Version 1. NCCN Clinical Practice Guidelines in Oncology. Retrieved from
  21. NCCN. (2020b). Non-Small Cell Lung Cancer, Version 6. NCCN Clinical Practice Guidelines in Oncology. Retrieved from
  22. NCCN. (2020c). Small Cell Lung Cancer, Version 4. NCCN Clinical Practice Guidelines in Oncology. Retrieved from
  23. NCCN. (2021a). Non-Small Cell Lung Cancer, Version 5. NCCN Clinical Practice Guidelines in Oncology. Retrieved from
  24. NCCN. (2021b). Small Cell Lung Cancer, Version 3. NCCN Clinical Practice Guidelines in Oncology. Retrieved from
  25. Ostrin, E. J., Sidransky, D., Spira, A., & Hanash, S. M. (2020). Biomarkers for Lung Cancer Screening and Detection. Cancer Epidemiol Biomarkers Prev, 29(12), 2411-2415. doi:10.1158/1055-9965.Epi-20-0865
  26. Pankratz, D. G., Choi, Y., Imtiaz, U., Fedorowicz, G. M., Anderson, J. D., Colby, T. V., . . . Martinez, F. J. (2017). Usual Interstitial Pneumonia Can Be Detected in Transbronchial Biopsies Using Machine Learning. Ann Am Thorac Soc, 14(11), 1646-1654. doi:10.1513/AnnalsATS.201612-947OC
  27. Planchard, D., Popat, S., Kerr, K., Novello, S., Smit, E., Faivre-Finn, C., . . . Hellmann, M. (2018). Metastatic non-small cell lung cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Annals of Oncology, 29, iv192-iv237.
  28. Postmus, P. E., Kerr, K. M., Oudkerk, M., Senan, S., Waller, D. A., Vansteenkiste, J., . . . on behalf of the, E. G. C. (2017). Early and locally advanced non-small-cell lung cancer (NSCLC): ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up†. Annals of Oncology, 28(suppl_4), iv1-iv21. doi:10.1093/annonc/mdx222
  29. Raghu, G., Flaherty, K. R., Lederer, D. J., Lynch, D. A., Colby, T. V., Myers, J. L., . . . Martinez, F. J. (2019). Use of a molecular classifier to identify usual interstitial pneumonia in conventional transbronchial lung biopsy samples: a prospective validation study. Lancet Respir Med, 7(6), 487-496. doi:10.1016/s2213-2600(19)30059-1
  30. Raghu, G., Mikacenic, Carmen. (2019). Pathogenesis of idiopathic pulmonary fibrosis. Retrieved from
  31. Raghu, G., Remy-Jardin, M., Myers, J. L., Richeldi, L., Ryerson, C. J., Lederer, D. J., . . . Wilson, K. C. (2018). Diagnosis of Idiopathic Pulmonary Fibrosis. An Official ATS/ERS/JRS/ALAT Clinical Practice Guideline. American Journal of Respiratory and Critical Care Medicine, 198(5), e44-e68. doi:10.1164/rccm.201807-1255ST
  32. Roncarati, R., Lupini, L., Miotto, E., Saccenti, E., Mascetti, S., Morandi, L., . . . Negrini, M. (2020). Molecular testing on bronchial washings for the diagnosis and predictive assessment of lung cancer. Mol Oncol, 14(9), 2163-2175. doi:10.1002/1878-0261.12713
  33. Silvestri, G. A., Vachani, A., Whitney, D., Elashoff, M., Porta Smith, K., Ferguson, J. S., . . . Spira, A. (2015). A Bronchial Genomic Classifier for the Diagnostic Evaluation of Lung Cancer. N Engl J Med, 373(3), 243-251. doi:10.1056/NEJMoa1504601
  34. Vaarwerk, B., Bisogno, G., McHugh, K., Brisse, H. J., Morosi, C., Corradini, N., . . . Merks, J. H. M. (2019). Indeterminate Pulmonary Nodules at Diagnosis in Rhabdomyosarcoma: Are They Clinically Significant? A Report From the European Paediatric Soft Tissue Sarcoma Study Group. Journal of Clinical Oncology, 37(9), 723-730. doi:10.1200/JCO.18.01535
  35. Veracyte. (2017). Retrieved from
  36. Veracyte. (2018). A genomic test that helps diagnose interstitial lung disease subtype. Retrieved from
  37. Veracyte. (2020). FOR PATIENTS. Retrieved from
  38. Weinberger, S., McDermott, Shaunagh. (2020). Diagnostic evaluation of the incidental pulmonary nodule. Retrieved from
  39. Whitney, D. H., Elashoff, M. R., Porta-Smith, K., Gower, A. C., Vachani, A., Ferguson, J. S., . . . Spira, A. (2015). Derivation of a bronchial genomic classifier for lung cancer in a prospective study of patients undergoing diagnostic bronchoscopy. BMC Med Genomics, 8, 18. doi:10.1186/s12920-015-0091-3

Coding Section 

Codes Number Description
CPT 0080U Oncology (Lung), Mass Spectrometric Analysis of Galectin-3-Binding Protein And Scavenger Receptor Cysteine-Rich Type 1 Protein M130, With Five Clinical Risk Factors (Age, Smoking Status, Nodule Diameter, Nodule-Spiculation Status and Nodule Location), Utilizing Plasma, Algorithm Reported as a Categorical Probability of Malignancy (effective 1/1/19)
  0092U Oncology (lung), three protein biomarkers, immunoassay using magnetic nanosensor technology, plasma, algorithm reported as risk score for likelihood of malignancy (effective 7/1/19)
  83520 Immunoassay for analyte other than infectious agent antibody or infectious agent antigen; quantitative, not otherwise specified
  81554 (effective 01/01/2021) Pulmonary disease (idiopathic pulmonary fibrosis (IPF), mRNA, gene expression analysis of 190 genes, utilizing transbronchial biopsies, diagnostic algorithm reported as categorical result (eg, positive or negative for high probability of usual interstitial pneumonia (UIP)) 
  84999 Unlisted chemistry procedure.
ICD-10-CM   Investigational for all relevant diagnoses
  R91.1 Solitary pulmonary nodule
ICD-10-PCS   Not applicable. ICD-10-PCS codes are only used for inpatient services. There are no ICD procedure codes for laboratory tests.
Type of Service Laboratory  
Place of Service Outpatient  

Procedure and diagnosis codes on Medical Policy documents are included only as a general reference tool for each Policy. They may not be all-inclusive. 

This medical policy was developed through consideration of peer-reviewed medical literature generally recognized by the relevant medical community, U.S. FDA approval status, nationally accepted standards of medical practice and accepted standards of medical practice in this community, Blue Cross and Blue Shield Association technology assessment program (TEC) and other non-affiliated technology evaluation centers, reference to federal regulations, other plan medical policies, and accredited national guidelines.

"Current Procedural Terminology © American Medical Association.  All Rights Reserved" 

History From 2017 Forward     


Annual review, no change to policy intent. Updating background, rationale and references. 


 Updating Coding Section with 2021 codes.


Annual review, updating policy to address genomic testing related to idiopathic pulmonary fibrosis. Also updating title, rationale and references. 


Annual review, no change to policy intent. Updating policy to change name of proteomic plasma assay to BDX-XL2. Updating regulatory status, rationale and references. 


Annual review date moved to October. No changes made. 


Annual review, no change to policy intent. Updating rationale and references. 


Updated review date. 


New Policy

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