Image analysis-based identification of high risk ER-positive, HER2-negative breast cancers
Given low rates of genomic testing uptake nationally, image-based methods may help identify ER+/HER2-patients who could benefit from testing.
Authors
- Katherine A. Hoadley (ORCID: https://orcid.org/0000-0002-1216-477X)
- Dong Neuck Lee
- Linnea T. Olsson (ORCID: https://orcid.org/0000-0002-2786-5966)
- Alina M. Hamilton (ORCID: https://orcid.org/0000-0001-9121-7114)
- Melissa A. Troester (ORCID: https://orcid.org/0000-0001-9506-6624)
- Benjamin C. Calhoun (ORCID: https://orcid.org/0000-0001-6505-5430)
- Yao Li (ORCID: https://orcid.org/0000-0002-7195-5774)
- J. S. Marron
Institutions
- University of North Carolina at Chapel Hill (US)
- University of North Carolina Health Care (US)
- UNC Lineberger Comprehensive Cancer Center
Publication Details
- Journal
- Breast Cancer Research
- Published
- 2024-12-04
- DOI
- https://doi.org/10.1186/s13058-024-01915-5
- Citations
- 2
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.69
Funders
- U.S. Department of Defense
- Breast Cancer Research Foundation
- Susan G. Komen
- National Institutes of Health
- Gillings School of Public Health
- Advanced Research Projects Agency
- National Cancer Institute
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill