International journal of computer assisted radiology and surgery · 2026
ProstNFound+: A Prospective Study using Medical Foundation Models for Prostate Cancer Detection.
СтатьяНазвание и аннотация: Перевод в очереди
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Wilson Paul F R, Harmanani Mohamed, To Minh Nguyen Nhat, Jamzad Amoon, Elghareb Tarek, Guo Zhuoxin, Kinnaird Adam, Wodlinger Brian, Abolmaesumi Purang, Mousavi Parvin
Аннотация
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 ( μ US) remains untested in clinical settings. We present ProstNFound+, an adaptation of FMs for PCa detection from μ US, 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 multicenter 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.
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 multicenter 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.