Evaluation of In-Office MRI/US Fusion Transperineal Prostate Biopsy via Free-Hand Device during Routine Clinical Practice.

Abstract Objectives To describe our recent experience with in-office transperineal prostate biopsy, including the adoption of software-assisted MRI/US fusion technology. Technological improvements have recently allowed transperineal biopsy to be effectively integrated into outpatient practices with negligible risk of infection. Methods We retrospectively reviewed a cohort of men undergoing transperineal prostate biopsy from 2018-2020, at a single institution. We compared this to another cohort of men undergoing transrectal fusion biopsy from 2014-2018, matched to the first cohort based on age, PSA, and presence of prostate cancer diagnosis prior to biopsy. All patients underwent systematic transperineal templated biopsies in addition to fusion biopsies of MRI-visible lesions. Baseline characteristics, MRI findings, biopsy results, and complications were analyzed and compared between the 2 groups. Results One-hundred and thirty men underwent transperineal prostate biopsy, and 130 men underwent transrectal fusion biopsy. Of those who underwent transperineal biopsy, 30% underwent fusion biopsy while all men with the transrectal biopsy underwent fusion biopsy. Men who underwent transperineal vs transrectal biopsy demonstrated lower infection rates (0% vs 0.8%, P = .31) with fewer prophylactic antibiotics prescribed at provider's discretion (48% vs 100%), yet higher total post-biopsy complication rates (6.1% vs 0.8%, P = .036). Conclusion Our initial experiences with transperineal prostate biopsy confirm prior findings demonstrating feasibility in outpatient urologic practice without infectious complication. Software-assisted MRI/US fusion technology can be successfully integrated with transperineal biopsies to target suspicious lesions. Higher rates of non–infectious complications were observed compared with transrectal biopsy. Further analysis is needed to determine whether risk profiles improve over the learning curve of this newly implemented approach.