Evidence map›Paper›PMID 40293532›Full record

ArticleWorld journal of urology2025

Optimizing biopsy decisions in PI-RADS 3 lesions: cross-institutional validation of a local clinical risk model.

Dominik Deniffel, Nathan Perlis, Sangeet Ghai, Emmanuel Salinas-Miranda, Khashayar Namdar, Laurence H Klotz, Alexandre Zlotta, Antonio Finelli, Masoom A Haider

Abstract readMulticenter StudyValidation Study
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In one paragraph

Article in World journal of urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Dominik DeniffelJoint Department of Medical Imaging, Princess Margaret Hospital, Sinai Health System, University of Toronto, Toronto, ON, Canada.
Nathan PerlisDivision of Urology, Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
Sangeet GhaiJoint Department of Medical Imaging, Princess Margaret Hospital, Sinai Health System, University of Toronto, Toronto, ON, Canada.
Emmanuel Salinas-MirandaJoint Department of Medical Imaging, Princess Margaret Hospital, Sinai Health System, University of Toronto, Toronto, ON, Canada.
Khashayar NamdarJoint Department of Medical Imaging, Princess Margaret Hospital, Sinai Health System, University of Toronto, Toronto, ON, Canada.
Laurence H KlotzDepartment of Surgery, Division of Urology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada.
Alexandre ZlottaDivision of Urology, Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
Antonio FinelliDivision of Urology, Department of Surgical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
Masoom A HaiderJoint Department of Medical Imaging, Princess Margaret Hospital, Sinai Health System, University of Toronto, Toronto, ON, Canada. m.haider@utoronto.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo compare a locally developed risk model based on clinical parameters with previously published models and strategies to reduce MRI-targeted biopsies of indeterminate PI-RADS 3 lesions without missing clinically significant prostate cancer (csPCa) (ISUP ≥ grade 2).

methodsRetrospective, two-center study including 278 patients without prior PCa who underwent multiparametric MRI and MRI-targeted biopsy. For robust parameter estimation, a risk model based on clinical parameters was developed in a high-prevalence cohort (institution 1; n = 202; PI-RADS 3-5) and recalibrated to PI-RADS 3 subgroup (n = 115). The validation cohort (institution 2, same metropolitan area) consisted of 76 men with PI-RADS 3 index lesions. Model performance was compared to previously suggested strategies and risk models using decision curve analysis.

resultsThe local risk model provided the highest net benefit across all clinically relevant risk thresholds in the validation cohort. At a 10% risk threshold, the model could safely avoid biopsies in 547 per 1,000 men with PI-RADS 3 index lesions without missing csPCa, outperforming other strategies in number of biopsies avoided: normalized ADC (223), PSA density (210), MRI-ERSPC risk calculator (164), lesion volume (55) and the Radtke risk model (0). At low risk thresholds < 10% both normalized ADC (0.81) and PSA density (0.08 ng/ml/ml) were clinically useful.

conclusionA locally fit risk model based on clinical parameters could safely reduce unnecessary biopsies in men with PI-RADS 3 index lesions, with normalized ADC and PSA density providing useful and easy-to-use alternatives.

Indexed as

Image-Guided BiopsyProstateProstatic NeoplasmsAgedHumansMagnetic Resonance ImagingMaleMiddle AgedMultiparametric Magnetic Resonance ImagingRetrospective StudiesRisk AssessmentCancer predictionMachine learningMagnetic resonance imagingProstate cancer

Identifiers

PMID40293532

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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.