ProstNFound+: A Prospective Study using Medical Foundation Models for Prostate Cancer Detection

Medical foundation models (FMs) offer a path to build high-performance diagnostic systems. However, their application to prostate cancer (PCa) detection from micro-ultrasound ( $\mu$ US) remains untested in clinical settings. We present ProstNFound+, an adaptation of FMs for PCa detection from $\mu$ US, along with its first prospective validation.

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).

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.

The results highlight its potential for clinical deployment, offering a scalable and interpretable alternative to expert-driven protocols.