Defining a Cohort that May Not Require Repeat Prostate Biopsy Based on PCA3 Score and Magnetic Resonance Imaging: The Dual Negative Effect

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Prostate cancer over diagnosis and overtreatment are concerns for clinicians and policy makers. Multiparametric magnetic resonance imaging and the PCA3 (prostate cancer antigen 3) urine test select for clinically significant cases. We explored how well the tests performed together with previous biopsies.

Materials and Methods:

In accordance with ethics committee approval we collected clinicopathological data on all patients in whom a PCA3 test was done from January 2011 to June 2016. This included patients on active surveillance for low risk prostate cancer and those without prostate cancer who had previous negative biopsies and suspicion of occult disease. We explored whether age, prostate specific antigen, PCA3 score, multiparametric magnetic resonance imaging, digital rectal examination, family history and prostate size would predict clinically significant prostate cancer on repeat biopsy. The negative predictive value of multiparametric magnetic resonance imaging and PCA3 score was calculated.


A total of 470 patients were included in study. The PCA3 score was abnormal at 35 or greater in 32.5% of cases. In the multivariate model including 154 men only age (OR 1.08, 95% CI 1.01–1.16), multiparametric magnetic resonance imaging PI-RADS™ (Prostate Imaging-Reporting and Data System) score 4 (OR 16.6, 95% CI 3.9–70.0) or 5 (OR 28.3, 95% CI 5.7–138) and PCA3 score (OR 2.9, 95% CI 1.0–8.8) predicted clinically significant cancer on biopsy. No patient with negative multiparametric magnetic resonance imaging and a normal PCA3 score had clinically significant prostate cancer on biopsy for a negative predictive value of 100% (p <0.0001).


In patients with dual negative tests (multiparametric magnetic resonance imaging and PCA3 score) clinically significant prostate cancer was never found on biopsy, which may be unnecessary in this group. This study was limited by its retrospective design, selection bias and lack of cost-effectiveness data.

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