Evidence map›Paper›PMID 39654290›Full record

ArticleBJU international2025

A Prostate Imaging-Reporting and Data System version 2.1-based predictive model for clinically significant prostate cancer diagnosis.

David G Gelikman, William S Azar, Enis C Yilmaz, Yue Lin, Luke A Shumaker, Andrew M Fang, Stephanie A Harmon, Erich P Huang, Sahil H Parikh, Jason A Hyman and 10 more

Abstract readValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BJU international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
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

20 authors.

David G GelikmanMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.ORCID https://orcid.org/0000-0002-4317-2561
William S AzarUrologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Enis C YilmazMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Yue LinMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Luke A ShumakerDepartment of Urology, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA.
Andrew M FangDepartment of Urology, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA.ORCID https://orcid.org/0000-0002-2390-7575
Stephanie A HarmonMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Erich P HuangBiometric Research Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Sahil H ParikhUrologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Jason A HymanUrologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Kyle SchuppeUrologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Jeffrey W NixDepartment of Urology, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA.
Samuel J GalganoO'Neal Comprehensive Cancer Center, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA.
Maria J MerinoLaboratory of Pathology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Peter L ChoykeMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Sandeep GurramUrologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Bradford J WoodCenter for Interventional Oncology, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Soroush Rais-BahramiDepartment of Urology, University of Alabama at Birmingham Heersink School of Medicine, Birmingham, AL, USA.ORCID https://orcid.org/0000-0001-9466-9925
Peter A PintoUrologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Baris TurkbeyMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.ORCID https://orcid.org/0000-0003-0853-6494

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo develop and validate a Prostate Imaging-Reporting and Data System (PI-RADS) version 2.1 (v2.1)-based predictive model for diagnosis of clinically significant prostate cancer (csPCa), integrating clinical and multiparametric magnetic resonance imaging (mpMRI) data, and compare its performance with existing models. PATIENTS AND

methodsWe retrospectively analysed data from patients who underwent prospective mpMRI assessment using the PI-RADS v2.1 scoring system and biopsy at our institution between April 2019 and December 2023. A 'Clinical Baseline' model using patient demographics and laboratory results and an 'MRI Added' model additionally incorporating PI-RADS v2.1 scores and prostate volumes were created and validated on internal and external patients. Both models were compared against two previously published MRI-based algorithms for csPCa using area under the receiver operating characteristic curve (AUC) and decision curve analysis.

resultsA total of 1319 patients across internal and external cohorts were included. Our 'MRI Added' model demonstrated significantly improved discriminative ability (AUC

conclusionOur PI-RADS v2.1-based mpMRI model significantly enhances csPCa prediction, outperforming the traditional clinical model in accuracy and reduction of unnecessary biopsies. It proves promising across diverse patient populations, establishing an updated, integrated approach for detection and management of prostate cancer.

Indexed as

Multiparametric Magnetic Resonance ImagingProstatic NeoplasmsAgedHumansMaleMiddle AgedPredictive Value of TestsProstateRetrospective Studiesbiopsymagnetic resonance imagingprostatic neoplasmsrisk assessmentstatistical model

Identifiers

PMID39654290
PMCPMC11975180

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.