ObjectivesTo develop and validate a predictive model based on clinical features and multiparametric magnetic resonance imaging (mpMRI) to reduce unnecessary systematic biopsies (SBs) in biopsy-naive patients with suspected prostate cancer (PCa).MethodsA total of 274 patients who underwent combined cognitive MRI-targeted biopsy (MRTB) with SB were retrospectively enrolled and temporally split into development (n = 201) and validation (n = 73) cohorts. Multivariable logistic regression analyses were used to determine independent predictors of clinically significant PCa (csPCa) on cognitive MRTB, and the clinical, MRI, and combined models were established respectively. Area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analyses were assessed.ResultsProstate imaging data and reporting system (PI-RADS) score, index lesion (IL) on the peripheral zone, age, and prostate-specific antigen density (PSAD) were independent predictors and included in the combined model. The combined model achieved the best discrimination (AUC 0.88) as compared to both the MRI model incorporated by PI-RADS score, IL level, and zone (AUC 0.86) and the clinical model incorporated by age and PSAD (AUC 0.70). The combined model also showed good calibration and enabled great net benefit. Applying the combined model as a reference for performing MRTB alone with a cutoff of 60% would reduce 43.8% of additional SB, while missing 2.9% csPCa.ConclusionsThe combined model based on clinical and mpMRI findings improved csPCa prediction and might be useful in making a decision about which patient could safely avoid unnecessary SB in addition to MRTB in biopsy-naive patients.Critical relevance statementThe combined model based on clinical and mpMRI findings improved csPCa prediction and might be useful in making a decision about which patient could safely avoid unnecessary SB in addition to MRTB in biopsy-naive patients.Key points center dot Age, PSAD, PI-RADS score, and peripheral index lesion were independent predictors of csPCa.center dot Risk models were used to predict the probability of detecting csPCa on cognitive MRTB.center dot The combined model might reduce 43.8% of unnecessary SBs, while missing 2.9% csPCa.Key points center dot Age, PSAD, PI-RADS score, and peripheral index lesion were independent predictors of csPCa.center dot Risk models were used to predict the probability of detecting csPCa on cognitive MRTB.center dot The combined model might reduce 43.8% of unnecessary SBs, while missing 2.9% csPCa.Key points center dot Age, PSAD, PI-RADS score, and peripheral index lesion were independent predictors of csPCa.center dot Risk models were used to predict the probability of detecting csPCa on cognitive MRTB.center dot The combined model might reduce 43.8% of unnecessary SBs, while missing 2.9% csPCa.