Cellular senescence predicts breast cancer risk from benign breast disease biopsy images
This study suggests that nuclear senescence scores predicted by deep learning models in breast epithelial and adipose tissue can predict the risk of breast cancer development among women with BBD.
Authors
- Rita Peila (ORCID: https://orcid.org/0000-0003-1784-7921)
- Indra Heckenbach (ORCID: https://orcid.org/0000-0001-7038-4677)
- Christopher C. Benz (ORCID: https://orcid.org/0000-0001-5479-4641)
- Sheila Weinmann (ORCID: https://orcid.org/0000-0002-7696-0070)
- Thomas E. Rohan (ORCID: https://orcid.org/0000-0001-6897-4045)
- Mark Powell (ORCID: https://orcid.org/0000-0001-9945-5360)
- Morten Scheibye‐Knudsen (ORCID: https://orcid.org/0000-0002-6637-1280)
- Yihong Wang (ORCID: https://orcid.org/0000-0003-1252-5579)
Institutions
- University of Copenhagen (DK)
- Albert Einstein College of Medicine (US)
- Buck Institute for Research on Aging (US)
- Rhode Island Hospital (US)
- Kaiser Permanente Center for Health Research (US)
- Zero Breast Cancer (US)
Publication Details
- Journal
- Breast Cancer Research
- Published
- 2025-03-11
- DOI
- https://doi.org/10.1186/s13058-025-01993-z
- Citations
- 1
- Primary Topic
- Telomeres, Telomerase, and Senescence
- Type
- article
- Field-Weighted Citation Impact
- 1.16
Funders
- Københavns Universitet