Department of Pulmonary Medicine, Cleveland Clinic, Respiratory Institute, Cleveland, OH, United States of America.
Division of Pulmonary and Critical Care, Wake Forest Baptist Health, Winston-Salem, NC, United States of America.
PLoS One. 2022 Jul 13;17(7):e0268567. doi: 10.1371/journal.pone.0268567. eCollection 2022.
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.
Percepta 基因组测序分类器 (GSC) 旨在提高和降低支气管镜检查非诊断性肺病变的恶性肿瘤风险。我们评估了 Percepta GSC 在不确定肺病变风险再分类中的性能。这项多中心研究包括来自 AEGIS I/II 队列和 Percepta 注册中心的当前或以前吸烟的疑似肺癌患者。分类器在支气管刷取的正常支气管上皮中进行测量。使用预定义的阈值计算敏感性、特异性和预测值。估计分类器减少不必要的侵入性程序的能力。一组 412 例患者纳入验证(恶性肿瘤患病率为 39.6%)。总体而言,29%的中度风险肺病变被降级为低风险,阴性预测值 (NPV) 为 91.0%,12.2%的中度风险病变被升级为高风险,阳性预测值 (PPV) 为 65.4%。此外,54.5%的低风险病变被降级为极低风险,NPV 超过 99%,27.3%的高风险病变被升级为极高风险,PPV 为 91.5%。如果在结节管理中使用分类器结果,可以避免 50%良性病变和 29%恶性病变患者进行额外的侵入性程序。Percepta GSC 作为排除和纳入测试均具有高度准确性。这种风险再分类的高度准确性可能会改善肺病变的管理。