GUIDE-US: grade-informed unpaired distillation of encoder knowledge from histopathology to micro-ultrasound.
[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 inf
- Sensitivity 60%
- Specificity 3.5%
APA
Elghareb T, Willis E, et al. (2026). GUIDE-US: grade-informed unpaired distillation of encoder knowledge from histopathology to micro-ultrasound.. International journal of computer assisted radiology and surgery. https://doi.org/10.1007/s11548-026-03630-2
MLA
Elghareb T, et al.. "GUIDE-US: grade-informed unpaired distillation of encoder knowledge from histopathology to micro-ultrasound.." International journal of computer assisted radiology and surgery, 2026.
PMID
42001367
Abstract
[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.
[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.