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Clinical validation and utility of Percepta GSC for the evaluation of lung cancer.
oleh: Peter Mazzone, Travis Dotson, Momen M Wahidi, Michael Bernstein, Hans J Lee, David Feller Kopman, Lonny Yarmus, Duncan Whitney, Christopher Stevenson, Jianghan Qu, Marla Johnson, P Sean Walsh, Jing Huang, Lori R Lofaro, Sangeeta M Bhorade, Giulia C Kennedy, Avrum Spira, M Patricia Rivera, AEGIS Study Team, Percepta Registry Investigators
| Format: | Article |
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| Diterbitkan: | Public Library of Science (PLoS) 2022-01-01 |
Deskripsi
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.