Defining the learning curve for multiparametric magnetic resonance imaging (MRI) of the prostate using MRI‐transrectal ultrasonography (TRUS) fusion‐guided transperineal prostate biopsies as a validation tool

• Published in: BJU international 2016-01, Vol.117 (1), p.80-86
• Main Authors: Gaziev, Gabriele, Wadhwa, Karan, Barrett, Tristan, Koo, Brendan C., Gallagher, Ferdia A., Serrao, Eva, Frey, Julia, Seidenader, Jonas, Carmona, Lina, Warren, Anne, Gnanapragasam, Vincent, Doble, Andrew, Kastner, Christof
• Format: Article
• Language: English
• Subjects: Aged; diagnosis; Humans; Image-Guided Biopsy - methods; image‐guided biopsy; learning curve; magnetic resonance imaging (MRI); Magnetic Resonance Imaging - methods; Male; Middle Aged; NMR; Nuclear magnetic resonance; Predictive Value of Tests; Prospective Studies; Prostate - diagnostic imaging; Prostate - pathology; Prostate cancer; prostatic neoplasm; Prostatic Neoplasms - diagnostic imaging; Prostatic Neoplasms - pathology; Ultrasonography
• ISSN: 1464-4096; 1464-410X
• DOI: 10.1111/bju.12892

Objectives To determine the accuracy of multiparametric magnetic resonance imaging (mpMRI) during the learning curve of radiologists using MRI targeted, transrectal ultrasonography (TRUS) guided transperineal fusion biopsy (MTTP) for validation. Patients and Methods Prospective data on 340 men who underwent mpMRI (T2‐weighted and diffusion‐weighted MRI) followed by MTTP prostate biopsy, was collected according to Ginsburg Study Group and Standards for Reporting of Diagnostic Accuracy standards. MRI data were reported by two experienced radiologists and scored on a Likert scale. Biopsies were performed by consultant urologists not ‘blinded’ to the MRI result and men had both targeted and systematic sector biopsies, which were reviewed by a dedicated uropathologist. The cohorts were divided into groups representing five consecutive time intervals in the study. Sensitivity and specificity of positive MRI reports, prostate cancer detection by positive MRI, distribution of significant Gleason score and negative MRI with false negative for prostate cancer were calculated. Data were sequentially analysed and the learning curve was determined by comparing the first and last group. Results We detected a positive mpMRI in 64 patients from Group A (91%) and 52 patients from Group E (74%). The prostate cancer detection rate on mpMRI increased from 42% (27/64) in Group A to 81% (42/52) in Group E (P < 0.001). The prostate cancer detection rate by targeted biopsy increased from 27% (17/64) in Group A to 63% (33/52) in Group E (P < 0.001). The negative predictive value of MRI for significant cancer (>Gleason 3+3) was 88.9% in Group E compared with 66.6% in Group A. Conclusion We demonstrate an improvement in detection of prostate cancer for MRI reporting over time, suggesting a learning curve for the technique. With an improved negative predictive value for significant cancer, decision for biopsy should be based on patient/surgeon factors and risk attributes alongside the MRI findings.