Large Language Model and Natural Language Processing Approach to Identify Cancer Recurrence From Pathology Reports
AutoNLP and Google Gemini 1.5 Pro are promising tools for identifying recurrence from pathology reports, with the latter demonstrating superior overall performance, making it particularly suitable for clinical translation.
Authors
- Mark Raymond Waddle (ORCID: https://orcid.org/0000-0002-6017-8850)
- Federico Mastroleo (ORCID: https://orcid.org/0000-0001-6580-6767)
- Javad Namazi
- David M. Routman (ORCID: https://orcid.org/0000-0002-2768-003X)
- Satomi Shiraishi (ORCID: https://orcid.org/0000-0002-9522-014X)
- D. Bayona
- Lydia Ekama
- Benjamin K. Talom
- Mariana Borras-Osorio
- Daniel K. Ebner
- Yuliia Lozko
Institutions
- University of Milan (IT)
- Mayo Clinic in Arizona (US)
- European Institute of Oncology (IT)
- American University of the Caribbean School of Medicine (SX)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2026-03-01
- DOI
- https://doi.org/10.1200/cci-25-00167
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00