Large-scale deep learning for metastasis detection in pathology reports
This study's finding demonstrate the feasibility of developing algorithms to automatically identify metastatic cancer cases from unstructured pathology reports.
Authors
- Antoinette M. Stroup (ORCID: https://orcid.org/0000-0003-3341-4018)
- John Gounley
- Patrycja Krawczuk (ORCID: https://orcid.org/0000-0003-4860-9619)
- Valentina I. Petkov (ORCID: https://orcid.org/0009-0009-8297-5381)
- Serban Negoita (ORCID: https://orcid.org/0000-0002-9327-9519)
- Stephen Mark Schwartz (ORCID: https://orcid.org/0000-0001-7499-8502)
- Lynne Penberthy (ORCID: https://orcid.org/0000-0001-9372-9869)
- Heidi A. Hanson (ORCID: https://orcid.org/0000-0003-0056-196X)
- Zachary Fox (ORCID: https://orcid.org/0000-0003-0138-2867)
- Elizabeth Hsu (ORCID: https://orcid.org/0000-0003-3762-9315)
- Jennifer A. Doherty (ORCID: https://orcid.org/0000-0002-1454-8187)
Institutions
- Rutgers, The State University of New Jersey (US)
- Oak Ridge National Laboratory (US)
- National Institutes of Health (US)
- Cape Town HVTN Immunology Laboratory / Hutchinson Centre Research Institute of South Africa (ZA)
- Huntsman Cancer Institute (US)
- Fred Hutch Cancer Center (US)
- National Cancer Institute (US)
Publication Details
- Journal
- JAMIA Open
- Published
- 2025-06-24
- DOI
- https://doi.org/10.1093/jamiaopen/ooaf070
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- U.S. Department of Energy
- National Institutes of Health
- National Cancer Institute
- Oak Ridge National Laboratory