Machine learning for predicting overall survival in early-stage supraglottic cancer: a SEER-based population study
Combining clinicopathological and treatment-related factors seems to predict accurately OS in patients with early-stage SGSCC. External independent geographic validation is warranted to evaluate model generalizability.
Authors
- Ilmo Leivo (ORCID: https://orcid.org/0000-0002-9609-1597)
- Mohammed Elmusrati (ORCID: https://orcid.org/0000-0001-9304-6590)
- Alhadi Almangush
- Antti A. Mäkitie
- Rasheed Omobolaji Alabi
Institutions
- Karolinska University Hospital (SE)
- University of Helsinki (FI)
- Helsinki University Hospital (FI)
- Turku University Hospital (FI)
- University of Vaasa (FI)
- Ministry of Higher Education and Scientific Research (AE)
Publication Details
- Journal
- Acta Oto-Laryngologica
- Published
- 2026-01-05
- DOI
- https://doi.org/10.1080/00016489.2025.2603595
- Primary Topic
- Head and Neck Cancer Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00