ProstNFound+: A Prospective Study using Medical Foundation Models for Prostate Cancer Detection
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866912678388170752 |
|---|---|
| author | Wilson, Paul F. R. Harmanani, Mohamed To, Minh Nguyen Nhat Jamzad, Amoon Elghareb, Tarek Guo, Zhuoxin Kinnaird, Adam Wodlinger, Brian Abolmaesumi, Purang Mousavi, Parvin |
| author_facet | Wilson, Paul F. R. Harmanani, Mohamed To, Minh Nguyen Nhat Jamzad, Amoon Elghareb, Tarek Guo, Zhuoxin Kinnaird, Adam Wodlinger, Brian Abolmaesumi, Purang Mousavi, Parvin |
| contents | Purpose: Medical foundation models (FMs) offer a path to build high-performance diagnostic systems. However, their application to prostate cancer (PCa) detection from micro-ultrasound (μUS) remains untested in clinical settings. We present ProstNFound+, an adaptation of FMs for PCa detection from μUS, along with its first prospective validation. Methods: ProstNFound+ incorporates a medical FM, adapter tuning, and a custom prompt encoder that embeds PCa-specific clinical biomarkers. The model generates a cancer heatmap and a risk score for clinically significant PCa. Following training on multi-center retrospective data, the model is prospectively evaluated on data acquired five years later from a new clinical site. Model predictions are benchmarked against standard clinical scoring protocols (PRI-MUS and PI-RADS). Results: ProstNFound+ shows strong generalization to the prospective data, with no performance degradation compared to retrospective evaluation. It aligns closely with clinical scores and produces interpretable heatmaps consistent with biopsy-confirmed lesions. Conclusion: The results highlight its potential for clinical deployment, offering a scalable and interpretable alternative to expert-driven protocols. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26703 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ProstNFound+: A Prospective Study using Medical Foundation Models for Prostate Cancer Detection Wilson, Paul F. R. Harmanani, Mohamed To, Minh Nguyen Nhat Jamzad, Amoon Elghareb, Tarek Guo, Zhuoxin Kinnaird, Adam Wodlinger, Brian Abolmaesumi, Purang Mousavi, Parvin Image and Video Processing Computer Vision and Pattern Recognition Purpose: Medical foundation models (FMs) offer a path to build high-performance diagnostic systems. However, their application to prostate cancer (PCa) detection from micro-ultrasound (μUS) remains untested in clinical settings. We present ProstNFound+, an adaptation of FMs for PCa detection from μUS, along with its first prospective validation. Methods: ProstNFound+ incorporates a medical FM, adapter tuning, and a custom prompt encoder that embeds PCa-specific clinical biomarkers. The model generates a cancer heatmap and a risk score for clinically significant PCa. Following training on multi-center retrospective data, the model is prospectively evaluated on data acquired five years later from a new clinical site. Model predictions are benchmarked against standard clinical scoring protocols (PRI-MUS and PI-RADS). Results: ProstNFound+ shows strong generalization to the prospective data, with no performance degradation compared to retrospective evaluation. It aligns closely with clinical scores and produces interpretable heatmaps consistent with biopsy-confirmed lesions. Conclusion: The results highlight its potential for clinical deployment, offering a scalable and interpretable alternative to expert-driven protocols. |
| title | ProstNFound+: A Prospective Study using Medical Foundation Models for Prostate Cancer Detection |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.26703 |