Predicting health-related quality of life two years post-diagnosis across seven cancer types: using machine learning to identify vulnerable patients
The predictors used in this analysis explained only 30% of the variation in long-term HRQoL. Similar to previous studies predicting HRQoL in cancer, these predictors miss crucial information. Baseline functioning, comorbidities, cancer type and BMI appeared to be the key vulnerability factors. Future studies should prioritize accurate prediction of low HRQoL scores.
Authors
- A.H.D.M. Dam
- Floortje Mols (ORCID: https://orcid.org/0000-0003-0818-2913)
- Anne M. May (ORCID: https://orcid.org/0000-0003-4857-8189)
- Koen van Benthem
- Geraldine R. Vink (ORCID: https://orcid.org/0000-0002-6731-9660)
- Belle H. de Rooij (ORCID: https://orcid.org/0000-0002-0172-0857)
- Rampal S. Etienne (ORCID: https://orcid.org/0000-0003-2142-7612)
- Dimitris Katsimpokis
- Willemijn F. Oudijk
- Helena M. Verkooijen
- Katja K. H. Aben
- Simone Oerlemans
Institutions
- Radboud University Nijmegen (NL)
- University of Groningen (NL)
- Utrecht University (NL)
- Tilburg University (NL)
- Radboud University Medical Center (NL)
- University Medical Center Utrecht (NL)
- Netherlands Federation of University Medical Centres (NL)
- Netherlands Comprehensive Cancer Organisation (NL)
Publication Details
- Journal
- Quality of Life Research
- Published
- 2026-02-12
- DOI
- https://doi.org/10.1007/s11136-026-04165-4
- Primary Topic
- Cancer survivorship and care
- Type
- article
- Field-Weighted Citation Impact
- 0.00