Evidence map›Paper›PMID 30842585›Full record

ArticleProstate cancer and prostatic diseases2019

Characterizing the learning curve of MRI-US fusion prostate biopsies.

Daniel Halstuch, Jack Baniel, David Lifshitz, Sivan Sela, Yaara Ber, David Margel

Registry-linked trialAbstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Prostate cancer and prostatic diseases, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06360523 (ProstateAI - A Prospective Paired Validating Cohort Study to Evaluate an MRI-based Optimized Prostate Cancer Diagnostic Pathway Powered by Artificial Intelligence), which is not on this map. Cited by 36 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed, 2 pooled it
3.5field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

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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.

2 · The registry

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.

NCT06360523 narecruitingnot on this mapstarted 2024, after this paper: background citation

ProstateAI - A Prospective Paired Validating Cohort Study to Evaluate an MRI-based Optimized Prostate Cancer Diagnostic Pathway Powered by Artificial Intelligence

TypeinterventionalSponsorChinese University of Hong KongRan2024 to 2028Enrolled368ConditionsProstate CancerArmsAI modal, Standard review
3 · Its place in the literature

Who cites it

36 citing papers in PubMed, 2 syntheses or guidelines pooled it, 53 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Daniel HalstuchDepartment of Urology, Rabin Medical Center, Petah Tikva, Israel. danielhalstuch@gmail.com.ORCID http://orcid.org/0000-0002-6331-5056
Jack BanielDepartment of Urology, Rabin Medical Center, Petah Tikva, Israel.
David LifshitzDepartment of Urology, Rabin Medical Center, Petah Tikva, Israel.
Sivan SelaDepartment of Urology, Rabin Medical Center, Petah Tikva, Israel.
Yaara BerDepartment of Urology, Rabin Medical Center, Petah Tikva, Israel.
David MargelDepartment of Urology, Rabin Medical Center, Petah Tikva, Israel.
Rabin Medical Center · ILTel Aviv University · IL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMRI-US fusion prostate biopsies are becoming a common procedure to diagnose prostate cancer. There is a paucity of information regarding the learning curve for fusion biopsies. We aim to study the amount of experience needed to be both accurate and time-efficient in this procedure.

methodsWe prospectively collected data on all MRI-US fusion biopsies performed from April 2014 to August 2017. We used two parameters to define the learning curve. Process Measurement (efficiency) was measured by time from the beginning of anesthesia to end of procedure. Outcome Measurement (accuracy) was measured by cancer detection rate for PI-RAD 3 lesions. The end of the learning curve was defined graphically and mathematically. We performed a separate analysis for transrectal and transperineal biopsies.

resultsWe completed 779 fusion biopsies (523 transrectal, 256 transperineal). Patients median age was 66 years (IQR 61-70) and median PSA 6.95 ng/ml (IQR 4.2-10.6). Prostate cancer was diagnosed in 385 (49%). Process Measurement-Procedure time decreased from 45 min in the first transrectal fusion biopsy to 15 min after 109 biopsies and remained stable (p < 0.0001). Time decreased from 55 min in the first transperineal biopsy to 18 min after 124 biopsies (p < 0.0001). Outcome Measurement-In transrectal fusion-biopsies detection rate for PI-RADS 3 lesions increased from 35 to 50% after 104 biopsies. In transperineal fusion-biopsies, detection rate increased from 40 to 55% after 119 cases for PI-RADS 3 lesions.

conclusionsWe measured the learning curve of fusion biopsies graphically and mathematically. We demonstrated that proficiency occurs after 110 transrectal and 125 transperineal fusion-biopsies.

Indexed as

Learning CurveAgedBiopsy, Large-Core NeedleHumansImage-Guided BiopsyMagnetic Resonance Imaging, InterventionalMaleMiddle AgedMultimodal ImagingOperative TimePatient Care TeamProspective StudiesProstateProstatic NeoplasmsRadiation OncologistsUltrasonography, Interventional

Identifiers

PMID30842585
OpenAlexW2919699073

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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.