Predicting Nottingham grade in breast cancer digital pathology using a foundation model
Our AI-based automated Nottingham grading system provides an efficient and reproducible tool for breast cancer assessment, offering potential for standardization of histologic grade in clinical practice.
Authors
- Doyeon An (ORCID: https://orcid.org/0000-0002-7331-5404)
- Suehyun Lee (ORCID: https://orcid.org/0000-0003-0651-6481)
- Jun Seo Kim (ORCID: https://orcid.org/0009-0006-9571-0209)
- Myung‐Giun Noh (ORCID: https://orcid.org/0000-0002-0646-1997)
- Y. H. Yeon (ORCID: https://orcid.org/0009-0006-3980-5078)
- Seok Jun Kim
- Jeong Hoon Lee (ORCID: https://orcid.org/0000-0002-1789-8270)
Institutions
- Gachon University (KR)
- Ajou University (KR)
- Stanford University (US)
Publication Details
- Journal
- Breast Cancer Research
- Published
- 2025-04-19
- DOI
- https://doi.org/10.1186/s13058-025-02019-4
- Citations
- 5
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 12.40
Funders
- Ajou University
- Korea Health Industry Development Institute