Habitat imaging radiomics of chest CT identifies noninfectious acute exacerbations in chronic obstructive pulmonary disease

Objective Noninfectious acute exacerbations of chronic obstructive pulmonary disease (AECOPD) pose significant diagnostic challenges due to the lack of reliable biomarkers. This study aims to develop and validate a CT-based habitat imaging radiomic model for precise identification of noninfectious AECOPD. Methods This retrospective study included 352 eligible chronic obstructive pulmonary disease (COPD) patients who received treatment at Tianjin Chest Hospital from January 2019 to December 2023. Among these patients (181 with noninfectious AECOPD, 171 with stable COPD), stratified randomization allocated cohorts to training ( n = 211) and testing ( n = 141) cohorts. Whole-lung CT scans were subjected to habitat mapping by voxel-wise K-means clustering, with radiomic features derived from habitat subregions and optimized using least absolute shrinkage and selection operator regression. Logistic regression (LR) and support vector machine (SVM) models combined habitat-derived traits with clinical factors. Results The CT-based whole lung was segmented into three habitat subregions: habitat subregion 1 (emphysema/bullae-associated), habitat subregion 2 (bronchovascular bundle), and habitat subregion 3 (lung parenchyma). The habitat total model showed predictive power for identifying noninfectious AECOPD (training: AUC = 0.853 [LR], 0.897 [SVM]; test: AUC = 0.800 [LR], 0.807 [SVM]). Multivariate analysis identified habitat total score and GOLD stage as independent predictors of noninfectious AECOPD ( p < 0.001). Conclusion In conclusion, this study segmented whole-lung CT scans of noninfectious AECOPD patients into habitat subregions, developing a radiomics model that demonstrated strong diagnostic efficacy. This approach provides an objective imaging biomarker and a potential tool for quantifying COPD heterogeneity.