膀胱病変の融合3D CT放射線画像と3Dオートエンコーダー深層特徴を用いた機械学習ベースのマルチクラス分類

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.