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This research is to develop a new tool to improve the performance of predicting prostate cancer (PCa) and reducing unnecessary biopsies. The clinical data of patients who were definitely diagnosed by prostate biopsy were retrospectively analyzed. PCa risks that include age, prostate-specific antigen (PSA), PSA density (PSAD), free-PSA (fPSA), the ratio of fPSA to PSA (%fPSA), prostate volume (PV), digital rectal examination (DRE) and multi-parametric magnetic resonance imaging (MP-MRI) were selected by univariate and multivariate analysis. The satisfactory risks were used to establish predictor (Prostate Biopsy Rating Scale, PBRS). The total score (TS) that was obtained from PBRS was performed to forecast PCa. The areas under the receiver operating characteristic curve (AUC) and the net reclassification index (NRI) were used to compare the predictive ability. A total of 1078 cases were recruited. The mean values of TS in PCa and non-PCa were 15.94 ± 3.26 and 10.49 ± 3.36 points respectively. The AUC of PBRS was higher than PSA, PSAD and MP-MRI (0.87 vs. 0.75, 0.78, 0.80, respectively). PBRS can reduce unnecessary biopsies compared with PSA, PSAD and MP-MRI by up to 63%, 54% and 44%, respectively. In brief, PBRS is a promising predictor of forecasting PCa.

A calculator based on prostate imaging reporting and data system version 2 (PI-RADS V2) is a promising prostate cancer predictor