Objective: To explore a predictive nomogram for the result of prostate biopsy based on Prostate Imaging Reporting and Data System version 2(PI-RADS v2)combined with prostate specific antigen (PSA) and its related parameters, and to assess its ability to diagnose prostate cancer by internal validation. Methods: We retrospectively analyzed the clinical data of 509 patients who underwent transrectal prostate biopsy guided by ultrasound during the period from January 2014 to December 2018 in the Department of Urology, First Affiliated Hospital of Xiamen University. In 509 cases, the mean age was (68.1±7.2) years. The mean prostate volume(PV) was (55.8±30.7) ml. The mean tPSA value was (19.86±18.94) ng/ml. The mean value of fPSA was (2.63±3.60) ng/ml and the mean f/tPSA was 0.14±0.08. The mean PSAD was (0.46±0.52) ng/ml2. Based on the PI-RADS v2, score 1 point have 37 cases, score 2 point have 131 cases, score 3 point have 152 cases, score 4 point have 102 cases, score 5 point have 87 cases. Of these patients, we randomly selected 80% (407 cases) as development group, and the other 20% (102 cases) as validation group. Univariate and multivariate logistic regression analysis of the development group was performed to identify the independent influence factors that can predict prostate cancer (PCa), thereby establishing a predictive model for the result of prostate biopsy. In the development group, validation group and tPSA was between 4.1-20.0 ng/ml, the model was evaluated by analyzing the receiver operating characteristic (ROC) curve, calibration curve and decision curve, and compared to PSA, fPSA, f/tPSA, PSAD, PI-RADS v2. Results: Among the 509 patients enrolled in the study, the detection rate of PCa was 43.0% (219/509). In the development group, the logistic regression analysis demonstrated that patient age (OR=1.113), f/tPSA (OR=0.004), PV (OR=0.986), PSAD (OR=11.023), digital rectal examination (DRE) texture (OR=2.295), transabdominal ultrasound (TAUS) with or without hypoechoic (OR=2.089), and PI-RADS v2 (OR=1.920) were independent factors for PCa (P<0.05). The nomogram based on all variables was established. In the development group, the area under the curve (AUC) of the model (0.883) was greater than those of tPSA (0.686), fPSA (0.593), f/tPSA (0.626), PSAD (0.777), PI-RADS v2 (0.761). In the validation group, the area under the curve of the model (0.839) was greater than those of tPSA (0.758), fPSA (0.666), f/tPSA (0.648), PSAD (0.832), PI-RADS v2 (0.803). In patients whose tPSA was between 4.1-20.0 ng/ml, the area under the curve of the model (0.801) was greater than those of tPSA (0.570), fPSA (0.426), f/tPSA (0.657), PSAD (0.707), PI-RADS v2 (0.701). The calibration curve of the nomogram indicated that the prediction curve was basically fitted to the standard curve, and the Hosmer-Lemeshow showed thatχ²=5.434, P=0.710, both suggested that the prediction model had better calibration ability. The decision curve showed that the model based on PI-RADS v2 had high clinical application value. Conclusions The nomogram based on PI-RADS v2 had a high predictive value for prostate cancer and could significantly improve the diagnostic performance. It had better diagnostic value than PSA and its related parameters. It also provided important guidance for the prostate cancer on clinical treatment of patients to some extent.