Machine learning for prediction of 30-day mortality in patients with advanced cancer: comparing pan-cancer and single-cancer models
A pan-cancer model enhanced the performance of short-term mortality estimates compared with models based on single cancer types. Thus, including multiple cancer types in predictive modeling in oncology could be advantageous when shared predictors are expected across cancer types.
Authors
- Charles Vesteghem (ORCID: https://orcid.org/0000-0003-2301-9081)
- A. Bjerrum
- Martin Bøgsted (ORCID: https://orcid.org/0000-0001-9192-1814)
- Laurids Østergaard Poulsen (ORCID: https://orcid.org/0000-0002-0466-976X)
- S. Bjerregaard-Michelsen
Institutions
- Aalborg University Hospital (DK)
- Copenhagen University Hospital (DK)
- Rigshospitalet (DK)
Publication Details
- Journal
- ESMO Real World Data and Digital Oncology
- Published
- 2025-05-08
- DOI
- https://doi.org/10.1016/j.esmorw.2025.100146
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.08
Funders
- Aalborg Universitet
- Aalborg Universitetshospital