Litcius/Paper detail

Clinical validation and utility of Percepta GSC for the evaluation of lung cancer

Peter J. Mazzone, Travis Dotson, Momen M. Wahidi, Michael Bernstein, Hans J. Lee, David Feller Kopman, Lonny Yarmus, Duncan Whitney, Christopher S. Stevenson, Jianghan Qu, Marla Johnson, P. Sean Walsh, Jing Huang, Lori Lofaro, Sangeeta Bhorade, Giulia C. Kennedy, Avrum Spira, M. Patricia Rivera, The AEGIS Study Team, The Percepta Registry Investigators

2022PLoS ONE13 citationsDOIOpen Access PDF

Abstract

The Percepta Genomic Sequencing Classifier (GSC) was developed to up-classify as well as down-classify the risk of malignancy for lung lesions when bronchoscopy is non-diagnostic. We evaluated the performance of Percepta GSC in risk re-classification of indeterminate lung lesions. This multicenter study included individuals who currently or formerly smoked undergoing bronchoscopy for suspected lung cancer from the AEGIS I/ II cohorts and the Percepta Registry. The classifier was measured in normal-appearing bronchial epithelium from bronchial brushings. The sensitivity, specificity, and predictive values were calculated using predefined thresholds. The ability of the classifier to decrease unnecessary invasive procedures was estimated. A set of 412 patients were included in the validation (prevalence of malignancy was 39.6%). Overall, 29% of intermediate-risk lung lesions were down-classified to low-risk with a 91.0% negative predictive value (NPV) and 12.2% of intermediate-risk lesions were up-classified to high-risk with a 65.4% positive predictive value (PPV). In addition, 54.5% of low-risk lesions were down-classified to very low risk with >99% NPV and 27.3% of high-risk lesions were up-classified to very high risk with a 91.5% PPV. If the classifier results were used in nodule management, 50% of patients with benign lesions and 29% of patients with malignant lesions undergoing additional invasive procedures could have avoided these procedures. The Percepta GSC is highly accurate as both a rule-out and rule-in test. This high accuracy of risk re-classification may lead to improved management of lung lesions.

Topics & Concepts

MedicineMalignancyLung cancerBronchoscopyPredictive valueRisk assessmentRadiologyPredictive value of testsInternal medicineComputer scienceComputer securityLung Cancer Diagnosis and TreatmentLung Cancer Research StudiesLung Cancer Treatments and Mutations