Upper-lobe CT imaging features improve prediction of lung function decline in COPD.

Background: 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. Methods: 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. Results: 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). Conclusion: 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.