Machine learning-based multi-class classification of bladder pathologies using fused 3D CT radiomic and 3D auto-encoder deep features.
Authors
Affiliations (4)
Affiliations (4)
- Department of Radiology, The Third Affiliated Hospital of Shenzhen University (Luohu People's Hospital), No. 47 Youyi Road, Luohu District, Shenzhen, Guangdong 518000, China.
- College of Computer Science and Software Engineering, Shenzhen University, No. 3688 Nanhai Avenue, Yuehai Campus, Nanshan District, Shenzhen, Guangdong 518060, China.
- Department of Radiology, Peking Union Medical College Hospital, Beijing 100730, China.
- Department of Radiology, Shenzhen Luohu Hospital of Traditional Chinese Medicine (Luohu Hospital Group), No. 16 Xiantong Road, Liantang Subdistrict, Luohu District, Shenzhen, Guangdong 518000, China.
Abstract
To develop an automated analytical framework that integrates hybrid radiomics and deep learning features from non-contrast CT images for the multi-class classification of bladder pathologies. This retrospective study analyzed 902 CT scans (584 normal, 142 calculi, 66 cancers, 110 cystitis). An integrated pipeline was implemented, comprising: 1) automatic bladder segmentation using a 3D-UNet, 2) hybrid feature extraction combining 100 radiomics features and 256 deep features from a 3D convolutional autoencoder, 3) feature selection via variance thresholding and LASSO regression, and 4) final classification using an XGBoost classifier. The dataset was split into training (80 %) and validation (20 %) sets. Performance was evaluated using the area under the receiver operating characteristic curve (AUROC) with a one-vs-rest strategy for multi-class classification. Model stability was assessed via stratified five-fold cross-validation, and interpretability was analyzed with SHapley Additive exPlanations (SHAP). The framework achieved one-vs-rest AUROCs of 0.94 (95 % CI: 0.89-0.99) for calculi, 0.92 (0.85-0.99) for cancer, 0.90 (0.84-0.95) for normal bladder, and 0.83 (0.75-0.91) for cystitis. The micro-average AUROC for four-class discrimination was 0.94 (0.92-0.96). Binary normal/abnormal classification demonstrated stable performance across cross-validation folds (AUROC range: 0.89-0.92). SHAP analysis revealed that radiomic features dominated decisions for calculi/normal differentiation, while deep features were critical for distinguishing cancer and cystitis. The proposed hybrid CT analysis framework achieves clinically relevant performance in the automated, multi-class classification of bladder pathologies, excelling particularly in calculi detection. The complementary roles of radiomic and deep features provide an interpretable diagnostic aid, demonstrating potential for integration into clinical workflows to support differential diagnosis.