Accurate diagnosis of pulmonary nodules using a noninvasive DNA methylation test

作者：Wenhua Liang, Zhiwei Chen, Caichen Li, Jun Liu, Jinsheng Tao, Xin Liu, Dezhi Zhao, Weiqiang Yin, Hanzhang Chen, Chao Cheng, Fenglei Yu, Chunfang Zhang, Luxu Liu, Hui Tian, Kaican Cai, Xiang Liu, Zheng Wang, Ning Xu, Qing Dong, Liang Chen, Yue Yang, Xiuyi Zhi, Hui Li, Xixiang Tu, Xiangrui Cai, Zeyu Jiang, Ji Hua, Lili Mo, Jiaxuan Wang, Jian‐Bing Fan, Jianxing He · 发表于：Journal of Clinical Investigation · 年份：2021 · DOI：10.1172/jci145973 · 被引用次数：122 · 研究领域：Lung Cancer Diagnosis and Treatment、Medical Imaging and Pathology Studies、Lung Cancer Treatments and Mutations

BACKGROUNDCurrent clinical management of patients with pulmonary nodules involves either repeated low-dose CT (LDCT)/CT scans or invasive procedures, yet causes significant patient misclassification. An accurate noninvasive test is needed to identify malignant nodules and reduce unnecessary invasive tests.METHODWe developed a diagnostic model based on targeted DNA methylation sequencing of 389 pulmonary nodule patients' plasma samples and then validation in 140 plasma samples independently. We tested the model in different stages and subtypes of pulmonary nodules.RESULTSA 100-feature model was developed and validated for pulmonary nodule diagnosis; the model achieved a receiver operating characteristic curve-AUC (ROC-AUC) of 0.843 on 140 independent validation samples, with an accuracy of 0.800. The performance was well maintained in (a) a 6 to 20 mm size subgroup (n = 100), with a sensitivity of 1.000 and adjusted negative predictive value (NPV) of 1.000 at 10% prevalence; (b) stage I malignancy (n = 90), with a sensitivity of 0.971; (c) different nodule types: solid nodules (n = 78) with a sensitivity of 1.000 and adjusted NPV of 1.000, part-solid nodules (n = 75) with a sensitivity of 0.947 and adjusted NPV of 0.983, and ground-glass nodules (n = 67) with a sensitivity of 0.964 and adjusted NPV of 0.989 at 10% prevalence. This methylation test, called PulmoSeek, outperformed PET-CT and 2 clinical prediction models (Mayo Clinic and Veterans Affairs) in discriminating malign...