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.

Determining the component-based operative time learning curve for robotic-assisted radical prostatectomy

Objectives:To determine the learning curve(LC)of total operative time and the discrete components of the robotic-assisted radical prostatectomy(RARP)for a recent robotic fellowship-trained urologic surgeon.Materials and methods:We performed a retrospective analysis of RARP procedures performed by a single new attending surgeon from August 2015 to April 2019.Patients'demographics and operative details were assessed.Total operative time was divided and prospectively recorded in 7 parts:(a)docking robot,(b)dissecting seminal vesicles(SVs)(c)dissecting endopelvic fascia(EPF),(d)incising bladder neck(BN),(e)completing the dissection,(f)lymph node dissection,and(g)urethrovesical anastomosis(UVA)and robot undocking.Cumulative sum analysis was used to ascertain the LC for total operative time and the 7 parts of the procedure.Results:One hundred twenty consecutive RARPs were performed.The LC was overcome at 25 cases for total operative time,13 cases for docking the robot,33 cases for dissecting SVs,31 cases for dissecting EPF,46 cases for incising BN,38 cases for prostate dissection,25 cases for lymph node dissection,and 52 cases for UVA.Total operative time was decreased 22.8%(p<0.0001)and time for robot docking,dissecting SVs,dissecting EPF,incising BN,completing prostate dissection,lymph node dissection,and UVA were decreased 16.7%,30.5%,29.5%,36.2%,37.3%,32.2%,and 26.9%,respectively(all p<0.05).Conclusions:We observed a 25-case LC for a fellowship-trained urologist to achieve stable operative performance of RARP surgery.Procedural components demonstrated variable LCs including the UVA that required upward of 52 cases.