Evidence map›Paper›PMID 42001367›Full record

ArticleInternational journal of computer assisted radiology and surgery2026

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

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

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Article in International journal of computer assisted radiology and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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5 · Who and what money

Authors and funding

9 authors.

Tarek Elghareb *University of British Columbia, Vancouver, Canada.
Emma Willis *Queen's University, Kingston, Canada. emma.willis@queensu.ca.ORCID http://orcid.org/0009-0007-5288-0176
Paul F R WilsonQueen's University, Kingston, Canada.
Minh Nguyen Nhat ToUniversity of British Columbia, Vancouver, Canada.
Mohammad Mahdi AbootorabiUniversity of British Columbia, Vancouver, Canada.
Amoon JamzadQueen's University, Kingston, Canada.
Brian WodlingerExact Imaging, Markham, Canada.
Parvin MousaviUniversity of British Columbia, Vancouver, Canada.
Purang AbolmaesumiUniversity of British Columbia, Vancouver, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeNon-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.

methodsWe 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.

resultsCompared 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%.

conclusionBy 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.

Indexed as

Image-Guided BiopsyProstatic NeoplasmsHumansMaleNeoplasm GradingSensitivity and SpecificityUltrasonographyCancer detectionMicro-USMultimodal deep learningProstate cancerUnpaired datasets

Identifiers

PMID42001367

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.