BACKGROUND. Current clinical management of patients with pulmonary nodules involves either repeated LDCT/CT scans or invasive procedures yet causes significant patient misclassification. An accurate non-invasive test is needed to identify malignant nodules and reduce unnecessary invasive tests. METHOD. We developed a diagnostic model based on targeted DNA methylation sequencing of 389 pulmonary nodule patients’ plasma samples, and then validated in 140 plasma samples independently. We tested the model in different stages and subtypes of pulmonary nodules. RESULTS. A 100-feature model was developed and validated for pulmonary nodule diagnosis: the model achieved a ROC-AUC of 0.843 on 140 independent validation samples with an accuracy of 0.800. The performance was well maintained in, 1) 6-20 mm size subgroup (N=100), with a sensitivity of 1.000 and adjusted NPV of 1.000 at 10% prevalence; 2) stage I malignancy (N=90), with a sensitivity of 0.971; 3) 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 two clinical prediction models (Mayo and Veterans Affairs) in discriminating malignant pulmonary nodules from benign ones. CONCLUSION. This study suggests that the blood-based DNA methylation model may provide a better test for classifying pulmonary nodules, which could help facilitate the accurate diagnosis of early-stage lung cancer from pulmonary nodule patients and guide clinical decisions. FUNDING. The National Key Research and Development Program of China; Science and Technology Planning Project of Guangdong Province; The National Natural Science Foundation of China National.
Wenhua Liang, Zhiwei Chen, Caichen Li, Jun Liu, Jinsheng Tao, Xin Liu, Dezhi Zhao, Weiqiang Yin, Hanzhang Chen, Chao Cheng, Fenglei Yu, Chunfang Zhang, Lunxu 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, Hua Ji, Lili Mo, Jiaxuan Wang, Jian-Bing Fan, Jianxing He