Lobar analysis of classical and deep learning radiomic features in chronic airflow limitation

Abstract Background Quantitative chest computed tomography (CT) enables detailed assessment of structural lung abnormalities in chronic airflow limitation (CAL). We evaluated whether lobe-based classical radiomics and frozen deep learning transfer features from inspiratory and expiratory CT improved discrimination of CAL compared with handcrafted quantitative measures. Methods In this population-based study, 2,579 participants underwent inspiratory and expiratory CT and post-bronchodilator spirometry. CAL, defined as post-bronchodilator FEV₁/FVC < 0.70, was the primary modeling endpoint and was present in 430 participants; 2,149 participants without CAL served as controls. Lung lobes were segmented using nnU-Net. Classical radiomic features were extracted with PyRadiomics, and deep learning transfer features were obtained from frozen 3D U-Net and SegResNet encoders pretrained on the non-lung AbdomenAtlas dataset. A previously developed handcrafted feature set was included as a reference. Elastic-net regularized logistic regression with 10-fold cross-validation was used for classification, and performance was assessed by the area under the receiver operating characteristic curve (AUC). Results Models based on expiratory features outperformed inspiratory-only models (AUC improvement 0.03–0.08). The best performance was achieved by classical radiomics combining inspiratory and expiratory features (AUC 0.87; positive predictive value 49%; negative predictive value 95%), exceeding the handcrafted model (AUC 0.81; PPV 34%; NPV 94%). Frozen transfer features from the non-lung-pretrained encoders showed slightly lower discrimination than classical radiomics, and combining feature types did not improve performance. Conclusion Lobe-based classical radiomics from combined inspiratory and expiratory CT improved cross-validated discrimination of spirometry-defined CAL in this population-based cohort, with expiratory imaging contributing substantially to optimal performance.