Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, 200003, People's Republic of China.
School of Medical Imaging, Weifang Medical University, Weifang, Shandong, 261053, People's Republic of China.
Int J Chron Obstruct Pulmon Dis. 2023 Jun 12;18:1169-1185. doi: 10.2147/COPD.S405429. eCollection 2023.
This study aimed to screen out computed tomography (CT) morphological features and clinical characteristics of patients with lung cancer to identify chronic obstructive pulmonary disease (COPD). Further, we aimed to develop and validate different diagnostic nomograms for predicting whether lung cancer is comorbid with COPD.
This retrospective study examined data from 498 patients with lung cancer (280 with COPD, 218 without COPD; 349 in training cohort, 149 in validation cohort) from two centers. Five clinical characteristics and 20 CT morphological features were evaluated. Differences in all variables were assessed between COPD and non-COPD groups. Models were developed using multivariable logistic regression to identify COPD, including clinical, imaging, and combined nomograms. Receiver operating characteristic curves were used to evaluate and compare the performance of nomograms.
Age, sex, interface, bronchus cutoff sign, spine-like process, and spiculation sign were independent predictors of COPD in patients with lung cancer. In the training and validation cohorts, the clinical nomogram showed good performance to predict COPD in lung cancer patients (areas under the curves [AUCs] of 0.807 [95% CI, 0.761-0.854] and 0.753 [95% CI, 0.674-0.832]); while the imaging nomogram showed slightly better performance (AUCs of 0.814 [95% CI, 0.770-0.858] and 0.780 [95% CI, 0.705-0.856]). For the combined nomogram generated with clinical and imaging features, the performance was further improved (AUC=0.863 [95% CI, 0.824-0.903], 0.811 [95% CI, 0.742-0.880] in the training and validation cohort). At 60% risk threshold, there were more true negative predictions (48 vs 44) and higher accuracy (73.15% vs 71.14%) for the combined nomogram compared with the clinical nomogram in the validation cohort.
The combined nomogram developed with clinical and imaging features outperformed clinical and imaging nomograms; this provides a convenient method to detect COPD in patients with lung cancer using one-stop CT scanning.
本研究旨在筛选出肺癌患者的计算机断层扫描（CT）形态特征和临床特征，以识别慢性阻塞性肺疾病（COPD）。此外，我们旨在开发和验证不同的诊断列线图，以预测肺癌是否合并 COPD。
本回顾性研究分析了来自两个中心的 498 例肺癌患者（280 例合并 COPD，218 例不合并 COPD；349 例在训练队列，149 例在验证队列）的数据。评估了 5 项临床特征和 20 项 CT 形态特征。评估 COPD 组和非 COPD 组之间所有变量的差异。使用多变量逻辑回归建立模型，以确定包括临床、影像学和联合列线图在内的 COPD。使用受试者工作特征曲线评估和比较列线图的性能。
年龄、性别、界面、支气管截断征、脊柱样过程和分叶征是肺癌患者 COPD 的独立预测因子。在训练和验证队列中，临床列线图对预测肺癌患者 COPD 具有良好的性能（曲线下面积[AUC]分别为 0.807[95%CI，0.761-0.854]和 0.753[95%CI，0.674-0.832]）；而影像学列线图表现稍好（AUC 分别为 0.814[95%CI，0.770-0.858]和 0.780[95%CI，0.705-0.856]）。对于使用临床和影像学特征生成的联合列线图，性能进一步提高（AUC=0.863[95%CI，0.824-0.903]，0.811[95%CI，0.742-0.880]在训练和验证队列中）。在验证队列中，与临床列线图相比，联合列线图在 60%的风险阈值下有更多的真阴性预测（48 比 44）和更高的准确性（73.15%比 71.14%）。
使用临床和影像学特征开发的联合列线图优于临床和影像学列线图；这为使用一站式 CT 扫描检测肺癌患者的 COPD 提供了一种方便的方法。
