基于计算机化断层扫描（CT）的辐射标记和临床及影像学特征，开发并验证预测肺癌患者慢性阻塞性肺病（COPD）的模型。我们回顾性地招募了443名接受肺功能测试的肺癌患者作为主要队列，按7:3比例随机分为训练（n = 311）或验证组（n = 132）。此外，还评估了54名独立的外部队列。采用最小绝对收缩和选择操作算法构建辐射学肺结节标记，利用逻辑回归选择关键变量开发临床和联合模型，并以示例图呈现。COPD与辐射标记在两个队列中显着相关。此外，该标记在多元回归分析中作为独立的COPD预测因素。对于训练、内部和外部队列，我们的辐射标记用于COPD预测的受试者工作特征曲线下面积（ROC，AUC）值分别为0.85、0.85和0.76。此外，辐射学提示图用于COPD预测的AUC值分别为0.927、0.879和0.762，表现优于其他两个模型。本研究提供一个结合辐射标记、临床和放射学特征的提示图，可用于预测肺癌患者COPD的风险，并进行一站式胸部CT扫描。版权 © 2023 The Association of University Radiologists。由Elsevier Inc.出版。保留所有权利。

To develop and validate a model for predicting chronic obstructive pulmonary disease (COPD) in patients with lung cancer based on computed tomography (CT) radiomic signatures and clinical and imaging features.We retrospectively enrolled 443 patients with lung cancer who underwent pulmonary function test as the primary cohort. They were randomly assigned to the training (n = 311) or validation (n = 132) set in a 7:3 ratio. Additionally, an independent external cohort of 54 patients was evaluated. The radiomic lung nodule signature was constructed using the least absolute shrinkage and selection operator algorithm, while key variables were selected using logistic regression to develop the clinical and combined models presented as a nomogram.COPD was significantly related to the radiomics signature in both cohorts. Moreover, the signature served as an independent predictor of COPD in the multivariate regression analysis. For the training, internal, and external cohorts, the area under the receiver operating characteristic curve (ROC, AUC) values of our radiomics signature for COPD prediction were 0.85, 0.85, and 0.76, respectively. Additionally, the AUC values of the radiomic nomogram for COPD prediction were 0.927, 0.879, and 0.762 for the three cohorts, respectively, which outperformed the other two models.The present study presents a nomogram that incorporates radiomics signatures and clinical and radiological features, which could be used to predict the risk of COPD in patients with lung cancer with one-stop chest CT scanning.Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.