Magnetic Resonance and Ultrasound Image Fusion Supported Transperineal Prostate Biopsy Using the Ginsburg Protocol: Technique, Learning Points, and Biopsy Results.

作者: Nienke Hansen , Giulio Patruno , Karan Wadhwa , Gabriele Gaziev , Roberto Miano

DOI: 10.1016/J.EURURO.2016.02.064

关键词: ProstateProstate cancerProstate biopsyBiopsyMedicineSampling (medicine)RadiologyCancerMagnetic resonance imagingUltrasound

摘要: Abstract Background Prostate biopsy supported by transperineal image fusion has recently been developed as a new method to the improve accuracy of prostate cancer detection. Objective To describe Ginsburg protocol for multiparametric magnetic resonance imaging (mpMRI) and transrectal ultrasound (TRUS) fusion, provide learning points its application, report results. The article is supplemented Surgery in Motion video. Design, setting, participants This single-centre retrospective outcome study included 534 patients from March 2012 October 2015. A total 107 had no previous biopsy, 295 benign TRUS-guided biopsies, 159 were on active surveillance low-risk cancer. Surgical procedure Likert scale reported mpMRI suspicion 1 (no suspicion) 5 (cancer highly likely). Transperineal biopsies obtained under general anaesthesia using BiopSee software (Medcom, Darmstadt, Germany). All systematic two cores each 12 anatomic sectors. 3–5 lesions targeted with further per lesion. Outcome measurements statistical analysis Any Gleason score 7–10 noted. Descriptive statistics positive predictive values (PPVs) negative (NPVs) calculated. Results limitations detection rate was similar across clinical groups. MRI 378 (71%) patients. Cancer detected 249 (66%) noted 157 (42%) these PPV detecting 0.15 3, 0.43 4, 0.63 5. NPV 1–2 findings 0.87 0.97 ≥4+3=7 Limitations include lack data complications. Conclusions MRI/TRUS yielded high rates Because excluding very high, may not be needed all men elevated prostate-specific antigen nonsuspicious mpMRI. Patient summary We present our technique sample (biopsy) route (the area between scrotum anus) detect images guide sampling.

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