Learning curve for magnetic resonance imaging/ultrasound fusion prostate biopsy in detecting prostate cancer using cumulative sum analysis

Background:Targeted magnetic resonance(MR)with ultrasound(US)fusion-guided biopsy has been shown to improve detection of prostate cancer.The implementation of this approach requires integration of skills from radiologists and urologists.Objective methods for assessment of learning curves,such as cumulative sum(CUSUM)analysis,may be helpful in identifying the presence and duration of a learning curve.The aim of this study is to determine the learning curve for MR/US fusion-guided biopsy in detecting clinically significant prostate cancer using CUSUM analysis.Materials and methods:Retrospective analysis was performed in this institutional review board-approved study.Two urologists implemented an MR/US fusion-guided prostate biopsy program between March 2015 and September 2017.The primary outcome measure was cancer detection rate(CDR)stratified by Prostate Imaging Reporting and Data System(PI-RADS)scores assigned on the MR imaging.Cumulative sum analysis quantified actual cancer detection versus a predetermined target satisfactory CDR of MR/US fusion biopsies in a sequential case-by-case basis.For this analysis,satisfactory performance was defined as>80%CDR in patients with Pl-RADS 5,>50%in PI-RADS 4,and<20%in Pl-RADS 1-3.Results:Complete data were available for MR/US fusion-guided biopsies performed on 107 patients.The CUSUM learning curve analysis demonstrated intermittent underperformance until approximately 50 cases.After this inflection point,there was consistently good performance,evidence that no further learning curve was being encountered.Conclusions:At a new center implementing MR/US fusion-guided prostate biopsy,the learning curve was approximately 50 cases before a consistently high performance for prostate cancer detection.

Validation of an MRI-based prostate cancer prebiopsy Gleason score predictive nomogram

Background:Gleason score grading is a cornerstone of risk stratification and management of patients with prostate cancer(PCa).In this work,we derive and validate a nomogram that uses prostate multiparametric magnetic resonance imaging(MP-MRI)and clinical patient characteristics to predict biopsy Gleason scores(bGS).Materials and methods:A predictive nomogram was derived from 143 men who underwent MP-MRI prior to any prostate biopsy and then validated on an independent cohort of 235 men from a different institution who underwent MP-MRI for PCa workup.Screen positive lesions were defined as lesions positive on T2W and DWI sequences on MP-MRI.Prostate specific antigen(PSA)density,number of screen positive lesions,and MRI suspicion were associated with PCa Gleason score on biopsy and were used to generate a predictive nomogram.The independent cohort was tested on the nomogram and the most likely bGS was noted.Results:The mean PSA in the validation cohort was 9.25ng/mL versus 6.8ng/mL in the original cohort(p=0.001).The distribution of Gleason scores between the 2 cohorts were not significantly different(p=0.7).In the original cohort of men,the most probable nomogram generated Gleason score agreed with actual pathologic bGS findings in 61%of the men.In the validation cohort,the most likely nomogram predicted bGS agreed with actual pathologic bGS 51%of the time.The nomogram correctly identified any PCa versus non-PCa 63%of the time and clinically significant(Gleason score≥7)PCa 69%of the time.The negative predictive value for clinically significant PCa using this prebiopsy nomogram was 74%in the validation group.Conclusions:A preintervention nomogram based on PSA and MRI findings can help narrow down the likely pathologic finding on biopsy.Validation of the nomogram demonstrated a significant ability to correctly identify the most likely bGS.This feasibility study demonstrates the potential of a prebiopsy prediction of bGS and based on the high negative predictive value,identification of men who may not need biopsies,which could impact future risk stratification for PCa.