AutoCOPD–A novel and practical machine learning model for COPD detection using whole-lung inspiratory quantitative CT measurements: a retrospective, multicenter study

作者：Fanjie Lin, Zili Zhang, Jian Wang, Cuixia Liang, Jiaxuan Xu, Xiansheng Zeng, Qingpeng Zeng, Huai Chen, Jiayu Zhuang, Yu Ma, Qiang Ma, Ruoyao Shi, Jingyi Xu, Yuanyuan Li, Yuan Liang, Xinguang Wei, Lulu Wu, Renjun Huang, Tianchi Xiao, Wenhua Liang, Jinping Zheng, Jianxing He, Yun Liu, Zhenyu Liang, Nanshan Zhong, Wenju Lu · 发表于：EClinicalMedicine · 年份：2025 · DOI：10.1016/j.eclinm.2025.103166 · 被引用次数：8 · 研究领域：Chronic Obstructive Pulmonary Disease (COPD) Research、Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis、Inhalation and Respiratory Drug Delivery

Background: The rate of diagnosis for chronic obstructive pulmonary disease (COPD) is low worldwide. Quantitative computed tomography (QCT) parameters add value to quantify alterations in airway and lung parenchyma for COPD. This study aimed to assess the performance of QCT features in COPD detection using a whole-lung inspiratory CT model. Methods: This multicenter retrospective study was performed on 4106 participants. The derivation cohort containing 1950 participants who enrolled in Guangzhou communities from August 2017 to December 2019, was separated for training and internal validation cohorts, and three external validation cohorts containing 1703 participants were recruited from the public hospitals (Cohort 1: the First Affiliated Hospital of Guangzhou Medical University; Cohort 2: Xiangyang central hospital; Cohort 3: the Second Affiliated Hospital of Xi'an Jiaotong University) in China between April 2017 and May 2024. Questionnaire information, CT reports, and QCT features derived from inspiratory CT were extracted for model development. A novel multimodal framework using eXtreme gradient boosting and hybrid feature selection was established for COPD detection. National Lung Screening Trial (NLST) cohort (n = 453) was applied to validate the multiracial extrapolation and robustness on low-dose CT scans. Findings: The QCT model (referred to as AutoCOPD) with ten features achieved the highest AUC of 0·860 (95% CI: 0·823-0·898) in the internal validation cohort, and sh...