Machine learning-based prediction of luminal breast cancer subtypes using polarised light microscopy
MMP, particularly its selected Mueller matrix elements, combined with clinical biomarkers show promise in distinguishing LBCS as validated against BluePrint®. By detecting subtle differences in tissue morphology, this approach may enhance breast cancer prognosis and help guide treatment decisions.
Authors
- I. Alex Vitkin (ORCID: https://orcid.org/0000-0001-5495-8888)
- Mohammadali Khorasani
- Sharon Nofech‐Mozes (ORCID: https://orcid.org/0000-0001-5450-9986)
- Kseniia Tumanova (ORCID: https://orcid.org/0000-0003-4106-3596)
Institutions
- Ontario Institute for Cancer Research (CA)
- University of British Columbia (CA)
- University of Toronto (CA)
- University of Victoria (CA)
Publication Details
- Journal
- British Journal of Cancer
- Published
- 2025-10-03
- DOI
- https://doi.org/10.1038/s41416-025-03150-x
- Primary Topic
- Optical Polarization and Ellipsometry
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Princess Margaret Hospital Foundation
- Universiteit Gent
- Canadian Institutes of Health Research
- Natural Sciences and Engineering Research Council of Canada