The identification and severity staging of chronic obstructive pulmonary disease using quantitative CT parameters, radiomics features, and deep learning features

Introduction: To evaluate the value of quantitative CT (QCT) parameters, radiomics features, and deep learning (DL) features based on inspiratory and expiratory CT for the identification and severity staging of chronic obstructive pulmonary disease (COPD). Methods: This retrospective analysis included 223 COPD patients and 59 healthy controls from the Guangzhou cohort. We stratified the participants into a training cohort and a testing cohort (7:3) and extracted DL features based on VGG-16 method, radiomics features based on pyradiomics package, and QCT parameters based on NeuLungCARE software. The Logistic regression method was employed to construct models for the identification and severity staging of COPD. The Shenzhen cohort was used as the external validation cohort to assess the generalizability of the models. Results: In the COPD identification models, Model 5-B1 (the QCT combined with DL model in biphasic CT) showed the best predictive performance with AUC of 0.920, and 0.897 in testing cohort and external validation cohort, respectively. In the COPD severity staging models, the predictive performance of Model 4-B2 (the model combining QCT with radiomics features in biphasic CT) and Model 5-B2 (the model combining QCT with DL features in biphasic CT was superior to that of the other models. Conclusion: This biphasic CT-based multi-modal approach integrating QCT, radiomics, or DL features offers a clinically valuable tool for COPD identification and severity staging.