Makimoto Kalysta, Virdee Sukhraj, Koo Meghan, Hogg James C, Bourbeau Jean, Tan Wan C, Kirby Miranda
Toronto Metropolitan University, Ontario, Canada.
Center for Heart, Lung Innovation, University of British Columbia, Vancouver, Canada.
ERJ Open Res. 2025 Jun 30;11(3). doi: 10.1183/23120541.00876-2024. eCollection 2025 May.
It is unknown whether prediction models for lung function decline using computed tomography (CT) imaging-derived features from the upper lobes improve performance compared with globally derived features in individuals at risk of and with COPD.
Individuals at risk (current or former smokers) and those with COPD from the Canadian Cohort Obstructive Lung Disease (CanCOLD) retrospective study, were investigated. A total of 103 CT features were extracted globally and regionally, and were used with 12 clinical features (demographics, questionnaires and spirometry) to predict rapid lung function decline for individuals at risk and those with COPD. Machine-learning models were evaluated in a hold-out test set using the area under the receiver operating characteristic curve (AUC) with DeLong's test for comparison.
A total of 780 participants were included (n=276 at risk; n=298 Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1 COPD; n=206 GOLD 2+ COPD). For predicting rapid lung function decline in those at risk, the upper-lobe CT model obtained a significantly higher AUC (AUC=0.80) than the lower-lobe CT model (AUC=0.63) and global model (AUC=0.66; p<0.05). For predicting rapid lung function decline in COPD, there was no significant differences between the upper-lobe (AUC=0.63), lower-lobe (AUC=0.59) or global CT features model (AUC=059; p>0.05).
CT features extracted from the upper lobes obtained significantly improved prediction performance compared with globally extracted features for rapid lung function decline in early/mild COPD.
对于慢性阻塞性肺疾病（COPD）风险人群及患者，与基于全肺CT影像特征构建的肺功能下降预测模型相比，利用上叶CT影像特征构建的模型是否能提高预测效能尚不清楚。
对来自加拿大慢性阻塞性肺疾病队列研究（CanCOLD）的风险人群（当前或既往吸烟者）及COPD患者进行研究。共提取了103项全肺及区域CT特征，并将其与12项临床特征（人口统计学、问卷及肺功能测定）一起用于预测风险人群及COPD患者的肺功能快速下降情况。使用机器学习模型在留出检验集中进行评估，采用受试者工作特征曲线下面积（AUC）并通过德龙检验进行比较。
共纳入780名参与者（风险人群n = 276；慢性阻塞性肺疾病全球倡议组织（GOLD）1级COPD患者n = 298；GOLD 2级及以上COPD患者n = 206）。对于预测风险人群的肺功能快速下降，上叶CT模型的AUC（AUC = 0.80）显著高于下叶CT模型（AUC = 0.63）和全肺模型（AUC = 0.66；p < 0.05）。对于预测COPD患者的肺功能快速下降，上叶（AUC = 0.63）、下叶（AUC = 0.59）或全肺CT特征模型之间无显著差异（AUC = 0.59；p > 0.05）。
与全肺提取的特征相比，早期/轻度COPD患者中，上叶提取的CT特征在预测肺功能快速下降方面具有显著改善的预测性能。
