Machine learning explainability for survival outcome in head and neck squamous cell carcinoma
The clinical implementation of an ML model can lead to individualized risk-based therapeutic decision-making. Therefore, validating these models with multi-institutional datasets and testing them in the context of clinical trials is warranted for safe clinical implementation.
Authors
- Antti A. Mäkitie (ORCID: https://orcid.org/0000-0002-0451-2404)
- Göran F. E. Laurell (ORCID: https://orcid.org/0000-0002-7760-246X)
- Alhadi Almangush (ORCID: https://orcid.org/0000-0003-4106-314X)
- Mohammed Elmusrati (ORCID: https://orcid.org/0000-0001-9304-6590)
- Ylva Tiblom Ehrsson (ORCID: https://orcid.org/0000-0001-7435-167X)
- Rasheed Omobolaji Alabi (ORCID: https://orcid.org/0000-0001-7655-5924)
Institutions
- Uppsala University (SE)
- Karolinska University Hospital (SE)
- University of Helsinki (FI)
- University of Turku (FI)
- Helsinki University Hospital (FI)
- University of Vaasa (FI)
Publication Details
- Journal
- International Journal of Medical Informatics
- Published
- 2025-03-08
- DOI
- https://doi.org/10.1016/j.ijmedinf.2025.105873
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.08
Funders
- Helsingin ja Uudenmaan Sairaanhoitopiiri
- Finska Läkaresällskapet
- Cancerfonden
- Sigrid Juséliuksen Säätiö
- Turun Yliopistollinen Keskussairaala