Objective To explore the value of a clinical-radiomics-deep learning(CRDL)fusion model based on biparametric mag-netic resonance imaging(bpMRI)in predicting biochemical recurrence(BCR)after radical prostatectomy(RP).Methods A retrospective analysis was conducted on 363 patients with prostate cancer(PCa)confirmed by RP pathology who underwent preoperative MRI,inclu-ding 84 cases experienced BCR(23.1％)and 279 cases did not experience BCR(76.9％).The patients were randomly divided into a training set(n=254)and a test set(n=109)in a ratio of 7∶3.Univariate Cox regression analysis was employed to select clinical variables related to BCR and the clinical model was constructed using backward stepwise multivariate Cox regression analysis.The radiomics features and deep learning(DL)features based on the DenseNet network were extracted.Radiomics and DL signatures were separately developed using least absolute shrinkage and selection operator(LASSO)-Cox regression algorithm.A CRDL fusion model was constructed by combining significant clinical features,DL signature and radiomics signature.The models'predictive performance for BCR was evaluated and compared using the concordance index(C-index).K-M survival curve and Log-rank test were used to assess the performance of CRDL fusion model in risk stratifica-tion of biochemical recurrence free survival(bRFS).Results In the test set,there was no statistically significant difference among C-index of radiomics signature,DL signature and clinical model(P＞0.05).The CRDL fusion model achieved a C-index of 0.83,higher than the clinical model,radiomics signature,and DL signature(P=0.03,0.01,and 0.03).K-M survival curve showed a significant difference in bRFS between low-risk and high-risk patients stratified by the CRDL fusion model[P＜0.000 1,hazard ratio(HR)=30.56,95％confidence interval(CI)10.64-87.75].Conclusion Radiomics signature and DL signature have comparable predictive per-formance for BCR after RP.The CRDL fusion model exhibits the best predictive efficacy for BCR,which is valuable for guiding postoperative treatment strategies in clinical practice.