Automated Identification of Breast Cancer Relapse in Computed Tomography Reports Using Natural Language Processing
We developed NLP models to identify local, regional, and distant breast cancer relapses from CT reports. Automating the identification of breast cancer relapses can enhance data collection about patient outcomes.
Authors
- Kathryn V. Isaac (ORCID: https://orcid.org/0000-0002-9374-031X)
- Gregory Arbour (ORCID: https://orcid.org/0009-0008-7323-4701)
- Alan Nichol (ORCID: https://orcid.org/0000-0002-4146-881X)
- Jaimie J. Lee (ORCID: https://orcid.org/0009-0009-6246-2773)
- Andres Zepeda
- Raymond T. Ng (ORCID: https://orcid.org/0000-0003-3692-8524)
Institutions
- University of British Columbia (CA)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1200/cci.24.00107
- Citations
- 3
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.70
Funders
- Faculty of Medicine, University of British Columbia