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Purpose <p>Medical foundation models (FMs) offer a path to build high-performance diagnostic systems. However, their application to prostate cancer (PCa) detection from micro-ultrasound (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\mu \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>μ</mi> </math></EquationSource> </InlineEquation>US) remains untested in clinical settings. We present ProstNFound+, an adaptation of FMs for PCa detection from <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\mu \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>μ</mi> </math></EquationSource> </InlineEquation>US, along with its first prospective validation.</p> Methods <p>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).</p> Results <p>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.</p> Conclusion <p>The results highlight its potential for clinical deployment, offering a scalable and interpretable alternative to expert-driven protocols.</p>

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

  • 期刊论文

  • 发表日期：

    2026年2月12日

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  • Paul F. R. Wilson,
  • Mohamed Harmanani,
  • Minh Nguyen Nhat To,
  • Amoon Jamzad,
  • Tarek Elghareb,
  • Zhuoxin Guo,
  • Adam Kinnaird,
  • Brian Wodlinger,
  • Purang Abolmaesumi,
  • Parvin Mousavi

摘要

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 ( \(\mu \) μ US) remains untested in clinical settings. We present ProstNFound+, an adaptation of FMs for PCa detection from \(\mu \) 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.