Multiparametric MRI‐Based Deep Learning Radiomics Model for Assessing 5‐Year Recurrence Risk in Non‐Muscle Invasive Bladder Cancer

Background Accurately assessing 5‐year recurrence rates is crucial for managing non‐muscle‐invasive bladder carcinoma (NMIBC). However, the European Organization for Research and Treatment of Cancer (EORTC) model exhibits poor performance. Purpose To investigate whether integrating multiparametric MRI (mp‐MRI) with clinical factors improves NMIBC 5‐year recurrence risk assessment. Study Type Retrospective. Population One hundred ninety‐one patients (median age, 65 years; age range, 54–73 years; 27 females) underwent mp‐MRI between 2011 and 2017, and received ≥5‐year follow‐ups. They were divided into a training cohort (N = 115) and validation/testing cohorts (N = 38 in each). Recurrence rates were 23.5% (27/115) in the training cohort and 23.7% (9/38) in both validation and testing cohorts. Field Strength/Sequence 3‐T, fast spin echo T2‐weighted imaging (T2WI), single‐shot echo planar diffusion‐weighted imaging (DWI), and volumetric spoiled gradient echo dynamic contrast‐enhanced (DCE) sequences. Assessment Radiomics and deep learning (DL) features were extracted from the combined region of interest (cROI) including intratumoral and peritumoral areas on mp‐MRI. Four models were developed, including clinical, cROI‐based radiomics, DL, and clinical‐radiomics‐DL (CRDL) models. Statistical Tests Student's t ‐tests, DeLong's tests with Bonferroni correction, receiver operating characteristics with the area under the curves (AUCs), Cox proportional hazard analyses, Kaplan–Meier plots, SHapley Additive ExPlanations (SHAP) values, and Akaike information criterion for clinical usefulness. A P ‐value <0.05 was considered statistically significant. Results The cROI‐based CRDL model showed superior performance (AUC 0.909; 95% CI: 0.792–0.985) compared to other models in the testing cohort for assessing 5‐year recurrence in NMIBC. It achieved the highest Harrell's concordance index (0.804; 95% CI: 0.749–0.859) for estimating recurrence‐free survival. SHAP analysis further highlighted the substantial role (22%) of the radiomics features in NMIBC recurrence assessment. Data Conclusion Integrating cROI‐based radiomics and DL features from preoperative mp‐MRI with clinical factors could improve 5‐year recurrence risk assessment in NMIBC. Evidence Level 3 Technical Efficacy Stage 3