{"ID":2848156,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.26703","arxiv_id":"2510.26703","title":"ProstNFound+: A Prospective Study using Medical Foundation Models for Prostate Cancer Detection","abstract":"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.","short_abstract":"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 fi...","url_abs":"https://arxiv.org/abs/2510.26703","url_pdf":"https://arxiv.org/pdf/2510.26703v1","authors":"[\"Paul F. R. Wilson\",\"Mohamed Harmanani\",\"Minh Nguyen Nhat To\",\"Amoon Jamzad\",\"Tarek Elghareb\",\"Zhuoxin Guo\",\"Adam Kinnaird\",\"Brian Wodlinger\",\"Purang Abolmaesumi\",\"Parvin Mousavi\"]","published":"2025-10-30T17:07:04Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.CV\"]","methods":"[]","has_code":false}