ArticleEuropean urology oncology2025
ProCUSNet: Prostate Cancer Detection on B-mode Transrectal Ultrasound Using Artificial Intelligence for Targeting During Prostate Biopsies.
Article in European urology oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 5 papers.
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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.
A Multicenter Study of a Deep Learning Model Based on Spatial Registration of Multimodal Imaging and Digital Pathology for Predicting Clinically Significant Prostate Cancer
Investigation of an Intelligent Centre-adaptive Multi-modal Fusion Framework (Cad- MMFF) to Overcome Unnecessary Prostate Biopsies and Optimize MRI Utilization: a Hybrid Retrospective-prospective Study
Who cites it
5 citing papers in PubMed.
- Enhancing foundation model transfer for prostate cancer detection with patch-level contrastive learning.NPJ digital medicine · 2026Article
- Ultrasound echogenicity is complementary to PI-RADS for risk stratification of clinically significant prostate cancer.Prostate cancer and prostatic diseases · 2026Article
- Meta learning optimized TabNet for small sample repeat prostate biopsy prediction.Discover oncology · 2026Article
- Artificial intelligence for prostate cancer detection and risk stratification using transrectal ultrasound: a narrative review.Gland surgery · 2026Review
- Utilization of artificial intelligence in prostate cancer detection: a comprehensive review of innovations in screening and diagnosis.Frontiers in immunology · 2025Review
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
BACKGROUND AND
objectiveTo assess whether conventional brightness-mode (B-mode) transrectal ultrasound images of the prostate reveal clinically significant cancers with the help of artificial intelligence methods.
methodsThis study included 2986 men who underwent biopsies at two institutions. We trained the PROstate Cancer detection on B-mode transrectal UltraSound images NETwork (ProCUSNet) to determine whether ultrasound can reliably detect cancer. Specifically, ProCUSNet is based on the well-established nnUNet frameworks, and seeks to detect and outline clinically significant cancer on three-dimensional (3D) examinations reconstructed from 2D screen captures. We compared ProCUSNet against (1) reference labels (n = 515 patients), (2) eight readers that interpreted B-mode ultrasound (n = 20-80 patients), and (3) radiologists interpreting magnetic resonance imaging (MRI) for clinical care (n = 110 radical prostatectomy patients). KEY FINDINGS AND LIMITATIONS: ProCUSNet found 82% clinically significant cancer cases with a lesion boundary error of up to 2.67 mm and detected 42% more lesions than ultrasound readers (sensitivity: 0.86 vs 0.44, p < 0.05, Wilcoxon test, Bonferroni correction). Furthermore, ProCUSNet has similar performance to radiologists interpreting MRI when accounting for registration errors (sensitivity: 0.79 vs 0.78, p > 0.05, Wilcoxon test, Bonferroni correction), while having the same targeting utility as a supplement to systematic biopsies. CONCLUSIONS AND CLINICAL IMPLICATIONS: ProCUSNet can localize clinically significant cancer on screen capture B-mode ultrasound, a task that is particularly challenging for clinicians reading these examinations. As a supplement to systematic biopsies, ProCUSNet appears comparable with MRI, suggesting its utility for targeting suspicious lesions during the biopsy and possibly for screening using ultrasound alone, in the absence of MRI.
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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.