Evidence map›Paper›PMID 42773205›Full record

ArticleProstate cancer and prostatic diseases2026

MUSegNet: Automated detection of clinically significant prostate cancer on micro-ultrasound.

Mirabela Rusu, Steve R Zhou, Lichun Zhang, Moon Hyung Choi, Sulaiman Vesal, Adam S Kinnaird, Wayne G Brisbane, Giovanni Lughezzani, Davide Maffei, Vittorio Fasulo and 5 more

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Article in Prostate cancer and prostatic diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

15 authors.

Mirabela RusuDepartment of Radiology, Stanford University, Stanford, CA, USA. mirabela.rusu@stanford.edu.ORCID http://orcid.org/0000-0002-1789-8270
Steve R ZhouDepartment of Urology, Stanford University, Stanford, CA, USA.
Lichun ZhangDepartment of Radiology, Stanford University, Stanford, CA, USA.
Moon Hyung ChoiDepartment of Urology, Stanford University, Stanford, CA, USA.
Sulaiman VesalDepartment of Urology, Stanford University, Stanford, CA, USA.
Adam S KinnairdDepartment of Urology, University of Alberta, Edmonton, AL, Canada.
Wayne G BrisbaneDepartment of Urology, University of California Los Angeles, Los Angeles, CA, USA.
Giovanni LughezzaniDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.ORCID http://orcid.org/0000-0003-3939-4521
Davide MaffeiDepartment of Urology, IRCCS Humanitas Research Hospital, Milan, Italy.
Vittorio FasuloDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
Patrick AlbersDepartment of Urology, University of Alberta, Edmonton, AL, Canada.ORCID http://orcid.org/0000-0002-8032-8393
Ahmed El-KaffasDepartment of Radiology, University of California San Diego, La Jolla, CA, USA.
Wei ShaoDepartment of Medicine, University of Florida, Gainesville, FL, USA.
Richard E FanDepartment of Urology, Stanford University, Stanford, CA, USA.
Geoffrey A SonnDepartment of Radiology, Stanford University, Stanford, CA, USA.ORCID http://orcid.org/0000-0002-0850-1956

Funding

Division of Cancer Prevention, National Cancer Institute (NCI Division of Cancer Prevention) OT2OD038017Division of Cancer Prevention, National Cancer Institute (NCI Division of Cancer Prevention) OT2OD038065Division of Cancer Prevention, National Cancer Institute (NCI Division of Cancer Prevention) R37 CA260346
6 · The paper itself

Abstract

backgroundMicro-ultrasound (microUS) is a non-inferior alternative to multi-parametric MRI for clinically significant prostate cancer (csPCa) diagnosis, yet cancer's subtle features result in missed cancers and large inter-reader variability. We seek to automate the detection of csPCa on microUS by developing the microUS Prostate Cancer Detection Network (MUSegNet).

methodsImplemented using the well-established segmentation framework, nnUnet, MUSegNet automatically outlines the prostate, transitional zone, and csPCa. Our study explored strategies to train such segmentation models given reduced data, which is common with new imaging modalities such as microUS and/or when labeling data is tedious. MUSegNet was trained using three datasets: (1) 53 microUS exams manually labeled using MRI and pathology references (53 patients), (2) 565 unlabeled microUS exams (106 patients), and (3) 2749 conventional b-mode ultrasound exams (2281 patients). We evaluated MUSegNet in 73 independent patients from two institutions, against manual labels and in direct comparison with six expert readers.

resultsMUSegNet was significantly better at detecting csPCa than the baseline models trained using manual labels (area under the receiver operating characteristic curve - AUROC; 0.85 vs. 0.73, Delong p-value < 0.01, sensitivity: 0.74 vs. 0.27, dice similarity coefficient: 0.29 vs. 0.09, Wilcoxon p-value < 0.001). MUSegNet was better at detecting posterior vs. anterior lesions (AUROC: 0.94 vs. 0.72, sensitivity: 0.86 vs. 0.49, Dice: 0.31 vs. 0.11) and outperformed the six human readers for all lesions (AUROC: 0.85 vs. 0.78, sensitivity: 0.74 vs. 0.63). While evaluation in a larger cohort is needed, MUSegNet found csPCA in 33% of cases with csPCa and MRI invisible cancers (1 in 3).

conclusionsMUSegNet reliably detected the extent of clinically significant prostate cancer on micro-ultrasound exams, paving the way for improving cancer detection for biopsy targeting and reducing inter-reader variability. MUSegNet was best at capturing posterior cancers, with some benefit for the challenging anterior cancers.

Identifiers

PMID42773205

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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.