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Related Experiment Video

Updated: Feb 13, 2026

An Orthotopic Murine Model of Human Prostate Cancer Metastasis
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ProstNFound+: A Prospective Study using Medical Foundation Models for Prostate Cancer Detection.

Paul F R Wilson1, Mohamed Harmanani2, Minh Nguyen Nhat To3

  • 1Queen's University, Kingston, Canada. paul.wilson@queensu.ca.

International Journal of Computer Assisted Radiology and Surgery
|February 12, 2026
PubMed
Summary
This summary is machine-generated.

ProstNFound+, a new AI model, shows promise for detecting prostate cancer (PCa) using micro-ultrasound (μUS). This AI tool was successfully validated in a clinical setting, demonstrating reliable performance.

Keywords:
Foundation modelsMicro-ultrasoundProspective dataProstate cancer

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Area of Science:

  • Artificial Intelligence in Medical Diagnostics
  • Medical Imaging Analysis
  • Prostate Cancer Detection

Background:

  • Medical Foundation Models (FMs) are emerging as powerful tools for developing high-performance diagnostic systems.
  • The clinical application of FMs for prostate cancer (PCa) detection using micro-ultrasound (μUS) has not yet been prospectively validated.
  • Existing diagnostic protocols for PCa can be complex and rely heavily on expert interpretation.

Purpose of the Study:

  • To present ProstNFound+, an adapted foundation model for PCa detection from μUS.
  • To conduct the first prospective clinical validation of an FM-based system for PCa detection using μUS.
  • To evaluate the model's performance against established clinical scoring systems.

Main Methods:

  • ProstNFound+ was developed using a medical FM with adapter tuning and a custom prompt encoder integrating PCa biomarkers.
  • The model was trained on retrospective, multicenter μUS data and prospectively evaluated at a new clinical site.
  • Model predictions, including cancer heatmaps and risk scores, were benchmarked against PRI-MUS and PI-RADS clinical protocols.

Main Results:

  • ProstNFound+ demonstrated strong generalization capabilities, with no performance degradation on prospective data compared to retrospective evaluation.
  • The model's predictions closely aligned with standard clinical scores (PRI-MUS and PI-RADS).
  • Interpretable heatmaps generated by ProstNFound+ corresponded accurately with biopsy-confirmed PCa lesions.

Conclusions:

  • ProstNFound+ shows significant potential for clinical deployment in prostate cancer detection.
  • The AI model offers a scalable and interpretable alternative to traditional expert-driven diagnostic protocols.
  • Prospective validation confirms the model's reliability and generalizability in a clinical setting.