# Prostate-Specific Foundation Models for Enhanced Detection of Clinically Significant Cancer

arXiv: https://arxiv.org/abs/2502.00366 · HTML: https://mlsanity.com/paper/2502.00366/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Feb 2025.

## Built on by (most cited first, 5 of 5)

- [Understanding the Transfer Limits of Vision Foundation Models](https://mlsanity.com/paper/2601.15888/) (2026)
- [ProFound: A moderate-sized vision foundation model for multi-task prostate imaging](https://mlsanity.com/paper/2603.03961/) (2026)
- [Maximizing T2-Only Prostate Cancer Localization from Expected Diffusion Weighted Imaging](https://mlsanity.com/paper/2604.00985/) (2026)
- [HyperCT: Low-Rank Hypernet for Unified Chest CT Analysis](https://mlsanity.com/paper/2604.03224/) (2026)
- [Compass: Prostate Cancer Detection Needs Multi-View Context](https://mlsanity.com/paper/2607.06919/) (2026)

## Abstract (arXiv)

Accurate prostate cancer diagnosis remains challenging. Even when using MRI, radiologists exhibit low specificity and significant inter-observer variability, leading to potential delays or inaccuracies in identifying clinically significant cancers. This leads to numerous unnecessary biopsies and risks of missing clinically significant cancers. Here we present prostate vision contrastive network (ProViCNet), prostate organ-specific vision foundation models for Magnetic Resonance Imaging (MRI) and Trans-Rectal Ultrasound imaging (TRUS) for comprehensive cancer detection. ProViCNet was trained and validated using 4,401 patients across six institutions, as a prostate cancer detection model on radiology images relying on patch-level contrastive learning guided by biopsy confirmed radiologist annotations. ProViCNet demonstrated consistent performance across multiple internal and external validation cohorts with area under the receiver operating curve values ranging from 0.875 to 0.966, significantly outperforming radiologists in the reader study (0.907 versus 0.805, p<0.001) for mpMRI, while achieving 0.670 to 0.740 for TRUS. We also integrated ProViCNet with standard PSA to develop a virtual screening test, and we showed that we can maintain the high sensitivity for detecting clinically significant cancers while more than doubling specificity from 15% to 38% (p<0.001), thereby substantially reducing unnecessary biopsies. These findings highlight that ProViCNet's potential for enhancing prostate cancer diagnosis accuracy and reduce unnecessary biopsies, thereby optimizing diagnostic pathways.