Prebiopsy Magnetic Resonance Imaging Followed by Combination Biopsy for Prostate Cancer Diagnosis Is Associated with a Lower Risk of Biochemical Failure After Treatment Compared to Systematic Biopsy Alone.

Abstract

Background: Prostate cancer (PCa) diagnosis remains a complex field of study. Multiparametric magnetic resonance imaging (mpMRI) technology presents opportunities to enhance diagnostic precision. While recent advances in imaging and biopsy techniques show promise, the oncological implications of prebiopsy magnetic resonance imaging (MRI) and combination biopsy (ComBx) are not fully understood. This retrospective study evaluates the potential clinical impact of prebiopsy MRI and ComBx on PCa treatment outcomes. Methods: We conducted a comprehensive review of treatment-naïve patients undergoing prostate biopsy and subsequent radiation therapy (RT) or radical prostatectomy at the University of Cincinnati Health Center (2014-2020). Patients were categorized into two cohorts: those with prebiopsy mpMRI and ComBx versus those with systematic biopsy (SBx) alone. Patients with prostate-specific antigen (PSA) > 20 ng/mL were excluded. Biochemical recurrence (BCR) was defined as PSA ≥ 0.2 ng/mL post-prostatectomy or ≥2 ng/mL above nadir post-RT. Results: This study included 518 patients (189 SBx, 329 ComBx) with a median follow-up of 19.1 months. Median patient ages were 65.9 years (SBx) and 64.6 years (ComBx). The overall BCR rate was 10% with significantly lower rates in the ComBx group compared to SBx (6.4% vs. 16.4%, p < 0.001). Multivariable Cox regression analysis showed patients undergoing prebiopsy mpMRI with ComBx were 63% less likely to experience BCR (HR: 0.37, 95%CI 0.20-0.70, p = 0.002). Conclusions: Prebiopsy MRI followed by ComBx demonstrated lower BCR rates, suggesting improved PCa diagnosis and risk stratification. These findings highlight the potential of advanced imaging and biopsy techniques to benefit the management of PCa. Further longitudinal studies are needed to confirm the long-term clinical benefits of this approach.