Development and validation of a deep learning radiomics model for predicting capsular invasion in small renal masses: a multicenter retrospective study.

Background: Preoperative prediction of renal capsule invasion in small renal masses (SRMs) is crucial for treatment planning but challenging on computed tomography (CT). This study developed a deep learning radiomics (DLR) model using CT to noninvasively predict capsule invasion in SRM. Methods: We analyzed 413 SRMs from three centers (July 2017 to September 2024). Data from Centers 1 (the First Affiliated Hospital of Shandong First Medical University) and 2 (the Third Affiliated Hospital of Shenzhen University) (330 patients, 57.27±11.58 years) comprised the training set, and Center 3 (the Union Hospital, Tongji Medical College, Huazhong University of Science and Technology) (83 patients, 57.67±10.76 years) served as the external test set. Radiomics and deep learning features were extracted using PyRadiomics and a pre-trained ResNet50. Feature selection used maximum relevance and minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO). Model performance was evaluated by the area under the curve (AUC), with interpretability assessed via SHapley Additive exPlanations (SHAP) and clinical utility by calibration and decision curves. Results: On the training set, the radiomics (Rad), deep transfer learning (DTL), and DLR models showed AUCs of 0.846, 0.890, and 0.855, respectively. On the external test set, corresponding AUCs were 0.746, 0.715, and 0.734. SHAP analysis revealed greater contribution from deep learning features. All models demonstrated good calibration and clinical utility. Conclusions: The DLR model is feasible for noninvasive prediction of renal capsule invasion in SRM. While not outperforming individual Rad or DTL models, it provides a valuable exploratory tool for preoperative assessment.