PI-RADS v2 and ADC values: is there room for improvement?

PurposeTo determine the diagnostic accuracy of ADC values in combination with PI-RADS v2 for the diagnosis of clinically significant prostate cancer (CS-PCa) compared to PI-RADS v2 alone.Materials and methodsThis retrospective study included 155 men whom underwent 3-Tesla prostate MRI and subsequent MR/US fusion biopsies at a single non-academic center from 11/2014 to 3/2016. All scans were performed with a surface coil and included T2, diffusion-weighted, and dynamic contrast-enhanced sequences. Suspicious findings were classified using Prostate Imaging Reporting and Data System (PI-RADS) v2 and targeted using MR/US fusion biopsies. Mixed-effect logistic regression analyses were used to determine the ability of PIRADS v2 alone and combined with ADC values to predict CS-PCa. As ADC categories are more practical in clinical situations than numeric values, an additional model with ADC categories of800 and>800 was performed.ResultsA total of 243 suspicious lesions were included, 69 of which were CS-PCa, 34 were Gleason score 3+3 PCa, and 140 were negative. The overall PIRADS v2 score, ADC values, and ADC categories are independent statistically significant predictors of CS-PCa (p<0.001). However, the area under the ROC of PIRADS v2 alone and PIRADS v2 with ADC categories are significantly different in both peripheral and transition zone lesions (p=0.026 and p=0.03, respectively) Further analysis of the ROC curves also shows that the main benefit of utilizing ADC values or categories is better discrimination of PI-RADS v2 4 lesions.ConclusionADC values and categories help to diagnose CS-PCa when lesions are assigned a PI-RADS v2 score of 4.