A study on predicting recurrence of non-muscle-invasive bladder cancer within 2 years using mp-MRI radiomics.

Non-muscle invasive bladder cancer (NMIBC) has a high rate of postoperative recurrence and the efficacy of existing clinical prediction models is limited. This study aimed to combine multiparametric magnetic resonance imaging (mp-MRI) radiomic features with clinical characteristics to construct a machine learning model for accurately predicting the risk of recurrence within 2 years postoperatively in NMIBC patients. Retrospectively including 183 NMIBC patients (57 in the recurrence group, 126 in the non-recurrence group), radiomic features from mp-MRI imaging (T2W, ADC, and enhancement sequences) were extracted. Through LASSO selection, 4 key imaging features (MajorAxisLength, SZNN, S/V, Skewness) and 6 clinical features based on the EAU 2021 risk stratification were identified to constitute the clinical-imaging dataset. Through comparison with 10 machine learning models, Support Vector Machine (SVM) performed the best (training set AUC = 0.973, validation set AUC = 0.891), with external independent validation (108 cases) showing AUCs of 0.88 and 0.87, demonstrating good generalization ability. A bar chart integrating radiomics score (Rad-Score) with clinical features provides an intuitive prognostic tool. The study indicates that the clinical-imaging radiomics model based on SVM significantly enhances the efficacy of NMIBC recurrence prediction, addressing the shortcomings of traditional risk assessment and offering a reliable basis for personalized postoperative management. Study limitations include the retrospective design and the absence of molecular biomarkers, necessitating future multicenter prospective validation.