A Risk Model for Detecting Clinically Significant Prostate Cancer Based on Clinical Parameters and the Prostate Imaging Reporting and Data System using Bi-parametric Magnetic Resonance Imaging in a Japanese Cohort.

Abstract Background:To selectively identify men with clinically significant prostate cancer (sPC) is pivotal issue. To develop a risk model for detecting sPC based on Prostate Imaging Reporting and Data System (PI-RADS) for bi-parametric magnetic resonance imaging (bpMRI) and clinical parameters in a Japanese cohort is expected beneficial.MethodsBetween January 2011 and December 2016, we retrospectively analyzed clinical parameters and bpMRI findings from 773 biopsy-naïve patients. A risk model was established using multivariate logistic regression analysis and was presented on a nomogram. Discrimination of the risk model was compared using the area under the receiver operating characteristic curve. Statistical differences between the predictive model and clinical parameters were analyzed using DeLong’s test.ResultssPC was detected in 343 men (44.3%). In the multivariate logistic regression analysis to predict sPC, age (P=0.002), log prostate-specific antigen (P<0.001), prostate volume (P<0.001) and PI-RADS scores (P<0.001) contributed significantly to the model. The risk model showed a higher area under the curve (0.862), than age (0.646), log prostate-specific antigen (0.652), prostate volume (0.697) and imaging scores (0.822). DeLong test results also showed that the novel risk model performed significantly better compared with those parameters (P<0.05).ConclusionsThis novel risk model performed significantly better compared with PI-RADS scores and other parameters alone, and is thus expected to provide benefits in making decisions to biopsy on suspicion of sPC.