A Systematic Review on Machine Learning Techniques for Survival Analysis in Cancer
The review included 196 studies, from which 39 comparable studies were used for the analysis. Improved predictive performance was seen from the use of ML in almost all cancer types. Predictive performance of the different ML methods varied across cancer types, and multi-task and deep learning methods appeared to yield superior performance; however, it was reported in only a minority of papers. This study also highlighted great variability in both methodologies and their implementations.
Authors
- Michael Cronin (ORCID: https://orcid.org/0009-0009-9884-0879)
- Eric Wolsztynski (ORCID: https://orcid.org/0000-0001-9770-9769)
- Shirin Moghaddam (ORCID: https://orcid.org/0000-0002-9965-1510)
- Autumn O’Donnell (ORCID: https://orcid.org/0000-0002-4787-7462)
Institutions
- Ollscoil na Gaillimhe – University of Galway (IE)
- University of Limerick (IE)
- University College Cork (IE)
- Data Fusion International (Ireland) (IE)
Publication Details
- Journal
- Cancer Medicine
- Published
- 2025-11-01
- DOI
- https://doi.org/10.1002/cam4.71375
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00