Prostate-Specific Foundation Models for Enhanced Detection of Clinically Significant Cancer
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2025
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| author | Lee, Jeong Hoon Li, Cynthia Xinran Jahanandish, Hassan Bhattacharya, Indrani Vesal, Sulaiman Zhang, Lichun Sang, Shengtian Choi, Moon Hyung Soerensen, Simon John Christoph Zhou, Steve Ran Sommer, Elijah Richard Fan, Richard Ghanouni, Pejman Song, Yuze Seibert, Tyler M. Sonn, Geoffrey A. Rusu, Mirabela |
| author_facet | Lee, Jeong Hoon Li, Cynthia Xinran Jahanandish, Hassan Bhattacharya, Indrani Vesal, Sulaiman Zhang, Lichun Sang, Shengtian Choi, Moon Hyung Soerensen, Simon John Christoph Zhou, Steve Ran Sommer, Elijah Richard Fan, Richard Ghanouni, Pejman Song, Yuze Seibert, Tyler M. Sonn, Geoffrey A. Rusu, Mirabela |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_00366 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Prostate-Specific Foundation Models for Enhanced Detection of Clinically Significant Cancer Lee, Jeong Hoon Li, Cynthia Xinran Jahanandish, Hassan Bhattacharya, Indrani Vesal, Sulaiman Zhang, Lichun Sang, Shengtian Choi, Moon Hyung Soerensen, Simon John Christoph Zhou, Steve Ran Sommer, Elijah Richard Fan, Richard Ghanouni, Pejman Song, Yuze Seibert, Tyler M. Sonn, Geoffrey A. Rusu, Mirabela Image and Video Processing Computer Vision and Pattern Recognition 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. |
| title | Prostate-Specific Foundation Models for Enhanced Detection of Clinically Significant Cancer |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.00366 |