Objective To explore the value of biparametric magnetic resonance imaging(bp-MRI)radiomics models in noninvasive prediction of high-risk prostate cancer.Methods A total of 320 patients with pathologically confirmed prostate cancer were retro-spectively selected,and all patients underwent bp-MRI before pathology,including T2WI and diffusion weighted imaging(DWI).Appar-ent diffusion coefficient(ADC)maps were extracted from DWI.All patients were divided into high-risk(Gleason score≥8)and medium-low risk(Gleason score ≤7)groups based on the Gleason score.Using 3D Slicer software,the entire prostate gland was outlined.Python software was used to calculate parameters,and the minimum redundancy maximum correlation and sequence back-ward elimination algorithms were used to extract and select radiomics features and to build a model.Three radiomics(T2 WI,DWI,ADC)models were constructed and verified by logistic regression(LR).The performance of the model was evaluated by area under the curve(AUC)of receiver operating characteristic(ROC)curve,specificity(SP),sensitivity(SE),and accuracy(ACC).An indi-vidual prediction model was established via the clinical data of 224 patients and bp-MRI features,and validated via the data of 96 patients.Results A total of 1 165 radiomics features were extracted.After feature screening,2,4 and 6 radiomics features were screened out to construct T2WI model,DWI model and ADC model for predicting high-risk prostate cancer.All radiomics models had significant predictive performance in identifying medium-low risk and high-risk groups(P＜0.05).The DWI model had the highest predictive value,and the AUC,ACC,SE,and SP in the training group were 0.814,0.756,0.838,and 0.744,respectively.The AUC,ACC,SE,and SP in the verification group were 0.840,0.756,0.848,and 0.784,respectively.Conclusion Radiomics based on bp-MRI can better identify medium-low risk and high-risk prostate cancer before surgery.