Mammographic classification of interval breast cancers and artificial intelligence performance
AI more frequently flagged and accurately localized interval breast cancer types that were mammographically visible at screening (missed or minimal signs) compared with true interval or occult cancers.
Authors
- William W.Y. Hsu (ORCID: https://orcid.org/0000-0002-5168-070X)
- James S Chalfant (ORCID: https://orcid.org/0000-0001-6385-604X)
- Tiffany Yu (ORCID: https://orcid.org/0000-0002-8722-2700)
- James Sayre (ORCID: https://orcid.org/0000-0001-6090-8713)
- Hannah S. Milch (ORCID: https://orcid.org/0009-0009-4046-038X)
- Christopher Sears
- Shabnam Mortazavi
- Joann G. Elmore (ORCID: https://orcid.org/0000-0002-7311-6835)
- Lucy Chow (ORCID: https://orcid.org/0000-0003-4084-442X)
- Melissa Joines
- Nazanin Yaghmai
- Cheryce Fischer
- Anne Hoyt
Institutions
- University of California, Los Angeles (US)
- University of California System (US)
- Oklahoma State University Center for Health Sciences (US)
Publication Details
- Journal
- JNCI Journal of the National Cancer Institute
- Published
- 2025-04-18
- DOI
- https://doi.org/10.1093/jnci/djaf103
- Citations
- 10
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 24.81
Funders
- National Institutes of Health
- Agency for Healthcare Research and Quality
- National Cancer Institute