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Purpose <p>Non-invasive grading of prostate cancer (PCa) from micro-ultrasound (micro-US) could expedite triage and guide biopsies toward the most aggressive regions, yet current models struggle to infer tissue micro-structure at coarse imaging resolutions.</p> Methods <p>We introduce an unpaired histopathology knowledge-distillation strategy that trains a micro-US encoder to emulate the embedding distribution of a pretrained histopathology foundation model, conditioned on International Society of Urological Pathology (ISUP) grades. Training requires no patient-level pairing or image registration, and histopathology inputs are not used at inference.</p> Results <p>Compared to the current state of the art, our approach increases sensitivity to clinically significant PCa (csPCa) at 60% specificity by 3.5% and improves overall sensitivity at 60% specificity by 1.2%.</p> Conclusion <p>By enabling earlier and more dependable cancer risk stratification solely from imaging, our method advances clinical feasibility. Source code is available at <a href="https://github.com/DeepRCL/GUIDE-US.">https://github.com/DeepRCL/GUIDE-US.</a></p>

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GUIDE-US: grade-informed unpaired distillation of encoder knowledge from histopathology to micro-ultrasound

  • 期刊论文

  • 发表日期：

    2026年4月19日

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  • Tarek Elghareb,
  • Emma Willis,
  • Paul F. R. Wilson,
  • Minh Nguyen Nhat To,
  • Mohammad Mahdi Abootorabi,
  • Amoon Jamzad,
  • Brian Wodlinger,
  • Parvin Mousavi,
  • Purang Abolmaesumi

摘要

Purpose

Non-invasive grading of prostate cancer (PCa) from micro-ultrasound (micro-US) could expedite triage and guide biopsies toward the most aggressive regions, yet current models struggle to infer tissue micro-structure at coarse imaging resolutions.

Methods

We introduce an unpaired histopathology knowledge-distillation strategy that trains a micro-US encoder to emulate the embedding distribution of a pretrained histopathology foundation model, conditioned on International Society of Urological Pathology (ISUP) grades. Training requires no patient-level pairing or image registration, and histopathology inputs are not used at inference.

Results

Compared to the current state of the art, our approach increases sensitivity to clinically significant PCa (csPCa) at 60% specificity by 3.5% and improves overall sensitivity at 60% specificity by 1.2%.

Conclusion

By enabling earlier and more dependable cancer risk stratification solely from imaging, our method advances clinical feasibility. Source code is available at https://github.com/DeepRCL/GUIDE-US.