RESUMO
CONTEXT.: Multiparametric magnetic resonance imaging (mpMRI) of prostate with targeted biopsy has enhanced detection of high-grade prostatic adenocarcinoma (HG PCa). However, utility of amount of HG PCa (Gleason pattern 4/5) in mpMRI-targeted biopsies versus standard 12-core biopsies in predicting adverse outcomes on radical prostatectomy (RP) is unknown. OBJECTIVE.: To examine the utility of amount of HG PCa in mpMRI-targeted biopsies versus standard 12-core biopsies in predicting adverse RP outcomes. DESIGN.: We performed a retrospective review of prostate biopsies that had corresponding RP, 1 or more mpMRI-targeted biopsy, and Grade Group 2 disease or higher. For the 169 cases identified, total millimeters of carcinoma and HG PCa and longest length HG PCa in a single core were recorded for 12-core biopsies and each set of mpMRI-targeted biopsies. For RP specimens, Gleason grade, extraprostatic extension, seminal vesicle involvement, and lymph node metastasis were recorded. The main outcome studied was prostate-confined disease at RP. A logistic regression model was used to test which pre-RP variables related to this outcome. RESULTS.: Univariate analysis showed significant associations with adverse RP outcomes in 5 of 8 quantifiable variables; longest millimeter HG PCa in a single 12-core biopsy, highest Grade Group in any core, and total millimeter HG in mpMRI-targeted biopsies showed no statistical association (P = .54, P = .13, and P = .55, respectively). In multivariate analysis, total millimeter carcinoma in all cores, highest Grade Group in any core, and longest millimeter HG PCa in a single mpMRI-targeted core provided additional predictive value (P < .001, P = .004, and P = .03, respectively). CONCLUSIONS.: Quantitation of HG PCa in mpMRI-targeted biopsies provides additional value over 12-core biopsies alone in predicting nonorgan confined prostate cancer at RP. Linear millimeters of HG PCa in mpMRI-targeted biopsies is a significant parameter associated with higher pathologic stage and could be of value in risk models.