ArticleCurrent urology2023
Learning curve for magnetic resonance imaging/ultrasound fusion prostate biopsy in detecting prostate cancer using cumulative sum analysis.
Article in Current urology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- Comparison of cognitive magnetic resonance-ultrasonography fusion prostate biopsy outcomes in left lateral decubitus vs lithotomy positions: a prospective randomized study cognitive magnetic resonance-ultrasonography fusion prostate biopsy.International urology and nephrology · 2025Trial
- Residents and Consultants Have Equal Outcomes When Performing Transrectal Fusion Biopsies: A Randomized Clinical Trial.Current oncology (Toronto, Ont.) · 2024Trial
- Procedure efficiency and diagnostic outcomes during the learning curve of transperineal MRI-guided prostate biopsy.BJUI compass · 2026Article
- Perspective transition from transrectal to transperineal MRI-fusion biopsy under local anesthesia - the institutional TREXIT learning curve of the new gold standard.Prostate cancer and prostatic diseases · 2026Review
- Article
- Impact of operator expertise on transperineal free-hand mpMRI-fusion-targeted biopsies under local anaesthesia for prostate cancer diagnosis: a multicenter prospective learning curve.World journal of urology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 PI-RADS 5, >50% in PI-RADS 4, and <20% in PI-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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.