Predicting overall survival in glioblastoma patients using machine learning: an analysis of treatment efficacy and patient prognosis
The application of machine learning to GBM metadata offers a robust approach to predicting patient survival. The study highlights the potential of ML models to enhance clinical decision-making and contribute to personalized treatment strategies, with a focus on accuracy, reliability, and interpretability.
Authors
- Răzvan Onciul
- Răzvan-Adrian Covache-Busuioc (ORCID: https://orcid.org/0000-0002-6034-2470)
- Mugurel Petrinel Rădoi (ORCID: https://orcid.org/0000-0002-6320-177X)
- Adrian Dumitru (ORCID: https://orcid.org/0000-0003-3784-7355)
- Matei Șerban
- Felix-Mircea Brehar
- Corneliu Toader (ORCID: https://orcid.org/0000-0003-2787-5686)
- Carla Crivoi
Institutions
- University of Bucharest (RO)
- Carol Davila University of Medicine and Pharmacy (RO)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-04-09
- DOI
- https://doi.org/10.3389/fonc.2025.1539845
- Citations
- 8
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 10.88