Clinical utility of receptor status prediction in breast cancer and misdiagnosis identification using deep learning on hematoxylin and eosin-stained slides
These findings demonstrate the utility of the system in diverse clinical settings and its potential to improve breast cancer diagnosis. Given the substantial focus of current guidelines on reducing false negative diagnoses, this study supports the integration of H&E-based machine learning tools into workflows for quality assurance.
Authors
- Alexandra Cretu
- Shachar Cohen
- Ran Schley
- Yoav Binenbaum (ORCID: https://orcid.org/0000-0001-9901-7782)
- António Polónia (ORCID: https://orcid.org/0000-0001-8312-1681)
- Edmond Sabo
- Tal Goldman
- Gil Shamai (ORCID: https://orcid.org/0000-0002-8099-3027)
- Ron Kimmel (ORCID: https://orcid.org/0000-0002-3180-7961)
- Keren Drumea
- Karin Stoliar
- Tal Neoran
Institutions
- Boston Children's Hospital (US)
- Harvard University (US)
- Fernando Pessoa University (PT)
- Technion – Israel Institute of Technology (IL)
- Universidade do Porto (PT)
- Emek Medical Center (IL)
- Carmel Medical Center (IL)
- Dana-Farber Cancer Institute (US)
- Clalit Health Services (IL)
Publication Details
- Journal
- Communications Medicine
- Published
- 2024-12-20
- DOI
- https://doi.org/10.1038/s43856-024-00695-5
- Citations
- 9
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 3.13
Funders
- Israel Science Foundation