Evidence map›Paper›PMID 40859774›Full record

ArticleBJU international2025

ProMUS-NET: Artificial intelligence detects more prostate cancer than urologists on micro-ultrasonography.

Steve R Zhou, Lichun Zhang, Moon Hyung Choi, Sulaiman Vesal, Adam Kinnaird, Wayne G Brisbane, Giovanni Lughezzani, Davide Maffei, Vittorio Fasulo, Patrick Albers and 4 more

Abstract readComparative 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. Review
  2. Review
  3. Micro-ultrasound for prostate cancer.Nature reviews. Urology · 2026
    Review
  4. Review
  5. Review
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

14 authors.

Steve R ZhouDepartment of Urology, Stanford School of Medicine, Palo Alto, CA, USA.ORCID 0000-0003-1169-8072
Lichun ZhangDepartment of Radiology, Stanford School of Medicine, Palo Alto, CA, USA.
Moon Hyung ChoiDepartment of Urology, Stanford School of Medicine, Palo Alto, CA, USA.ORCID 0000-0001-5962-4772
Sulaiman VesalDepartment of Urology, Stanford School of Medicine, Palo Alto, CA, USA.
Adam KinnairdDepartment of Urology, University of Alberta, Edmonton, AB, Canada.ORCID 0000-0002-4115-4327
Wayne G BrisbaneDepartment of Urology, UCLA, Los Angeles, CA, USA.ORCID 0000-0003-0470-5262
Giovanni LughezzaniDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
Davide MaffeiDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.ORCID 0000-0001-5012-7219
Vittorio FasuloDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
Patrick AlbersDepartment of Urology, University of Alberta, Edmonton, AB, Canada.
Richard E FanDepartment of Urology, Stanford School of Medicine, Palo Alto, CA, USA.
Wei ShaoDepartment of Medicine, University of Florida, Gainesville, FL, USA.
Geoffrey A SonnDepartment of Urology, Stanford School of Medicine, Palo Alto, CA, USA.
Mirabela RusuDepartment of Urology, Stanford School of Medicine, Palo Alto, CA, USA.

Funding

Rad-pathomic deep learning models to assist radiologists in differentiating aggressive from indolent prostate cancer on MRIR37CA260346 · NCI · STANFORD UNIVERSITY · PI Mirabela Rusu · 2022 to 2026
$2.8M
NCI NIH HHS R37 CA260346
6 · The paper itself

Abstract

objectivesTo improve sensitivity and inter-reader consistency of prostate cancer localisation on micro-ultrasonography (MUS) by developing a deep learning model for automatic cancer segmentation, and to compare model performance with that of expert urologists. PATIENTS AND

methodsWe performed an institutional review board-approved prospective collection of MUS images from patients undergoing magnetic resonance imaging (MRI)-ultrasonography fusion guided biopsy at a single institution. Patients underwent 14-core systematic biopsy and additional targeted sampling of suspicious MRI lesions. Biopsy pathology and MRI information were cross-referenced to annotate the locations of International Society of Urological Pathology Grade Group (GG) ≥2 clinically significant cancer on MUS images. We trained a no-new U-Net model - the Prostate Micro-Ultrasound Network (ProMUS-NET) - to localise GG ≥2 cancer on these image stacks in a fivefold cross-validation. Performance was compared vs that of six expert urologists in a matched sub-cohort.

resultsThe artificial intelligence (AI) model achieved an area under the receiver-operating characteristic curve of 0.92 and detected more cancers than urologists (lesion-level sensitivity 73% vs 58%; patient-level sensitivity 77% vs 66%). AI lesion-level sensitivity for peripheral zone lesions was 86.2%.

conclusionsOur AI model identified prostate cancer lesions on MUS with high sensitivity and specificity. Further work is ongoing to improve margin overlap, to reduce false positives, and to perform external validation. AI-assisted prostate cancer detection on MUS has great potential to improve biopsy diagnosis by urologists.

Indexed as

Artificial IntelligenceDeep LearningProstatic NeoplasmsUrologistsAgedHumansImage-Guided BiopsyMagnetic Resonance ImagingMaleMiddle AgedProspective StudiesSensitivity and SpecificityUltrasonographyartificial intelligenceconvolutional neural networksmicro‐ultrasonographyprostate cancertargeted biopsy

Identifiers

PMID40859774
PMCPMC12633459

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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.