23 Scopus citations


Objective: To evaluate the predictive value of magnetic resonance imaging (MRI)-defined prostate-specific antigen (PSA) density and MRI interpretation for the detection of clinically significant prostate cancer (PCa). Methods: We retrospectively reviewed our institutional database of men who received prostate MRI prior to biopsy at our institution between September 2014 and December 2016, excluding those on active surveillance (n = 372). Logistic regression models to predict clinically significant PCa on biopsy were developed using (1) MRI-defined PSA density; (2) PSA alone; and (3) the Prostate Cancer Prevention Trial (PCPT) risk calculator. Additional logistic regression models were then generated by combining the previous clinical variables with MRI interpretation (ie, prostate imaging reporting and data system [PI-RADS] classification): (1) MRI-defined PSA density + MRI interpretation; (2) PSA + MRI interpretation; and (3) PCPT + MRI interpretation. Receiving operator characteristic curves for each of the 6 models were generated, and the area under the curves (AUC) were compared. Results: MRI-defined PSA density (AUCPSAD = 0.77) significantly outperformed the PSA (AUCPSA = 0.66, P <.01) and PCPT models (AUCPCPT = 0.70, P <.01). When combined with MRI interpretation (ie, PI-RADS classification), the MRI-defined PSA density model (AUCPSAD + MRI = 0.80) significantly outperformed the PSA (AUCPSA + MRI = 0.75, P <.01) and PCPT models (AUCPCPT + MRI = 0.76, P =.01). Conclusion: In addition to the PI-RADS classification, prebiopsy multiparametric magnetic resonance imaging also provides an accurate assessment of prostate volume. By utilizing this additional data to determine MRI-defined PSA density, we find that risk discrimination for clinically significant PCa on biopsy can be significantly improved.

Original languageEnglish
Pages (from-to)152-157
Number of pages6
StatePublished - Apr 2019


Dive into the research topics of 'Magnetic Resonance Imaging-Defined Prostate-Specific Antigen Density Significantly Improves the Risk Prediction for Clinically Significant Prostate Cancer on Biopsy'. Together they form a unique fingerprint.

Cite this