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Background: In order to improve postoperative functional outcome, including urinary continence and erectile function, nerve sparing surgery is recommended for patients with clinically localized prostate cancer (PCa). However, due to poor diagnosis accuracy at the preoperative stage, upstaging occurs in a considerable proportion of patients. Multiparametric magnetic resonance imaging (mpMRI) and the Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) have recently shown excellent performance in diagnosis and staging of PCa. The aim of this study was to develop a predictive model based on PI-RADS v2 for postoperative upstaging in patients with low-intermediate risk PCa. Methods: The medical records of 314 patients with low-intermediate risk PCa [prostate-specific antigen (PSA) level ≤20 ng/mL, Gleason score (GS) <8, and clinical stage < T3] who underwent preoperative mpMRI and radical prostatectomy in the Department of Urology, Peking University First Hospital between January 2012 and July 2019 were reviewed retrospectively. Clinicopathological characteristics were collected.All MRI reports were done at our institution as part of routine clinical practice before prostate biopsy and there was no re-reporting occurred. Using PI-RADS v2, the mpMRI results were assigned to three groups: "negative", "suspicious", and "positive". Multivariate logistic regression analysis was used to assess factors associated with postoperative pathological upstaging, defined as the presence of pT3 at final pathology. A regression coefficient based model for predicting postoperative upstaging was constructed and internally validated using 1,000 bootstrap resamples. The performance of the model was assessed using the area under the receiver operating characteristic curve (AUC). With the optimal cutoff point the performance of the model was assessed through analysis of sensitivity, specificity, positive predictive value, and negative predictive value.Results: Upstaging was observed in 119 (37.9%) patients. The univariate and multivariate analyses revealed that PSA density, biopsy Gleason grade group (GGG), and mpMRI findings were significantly independent predictors for postoperative upstaging (all P<0.05). A predictive model showing very favorable calibration characteristics and higher accuracy than the single variables was constructed (AUC =0.74; P<0.001). At the optimal cutoff point, the model demonstrated a sensitivity and negative predictive value of 87.4% and 87.0%, respectively. Conclusions: PI-RADS v2 assessment proved to be one of the most valuable predictors for postoperative upstaging in patients with low-intermediate risk PCa. The predictive model, based on PI-RADS v2 assessment, PSA density, and biopsy GGG, may help to select suitable candidates for nerve sparing radical 438 Song et al. Predictive model for nerve-sparing patients' selection.