Scoping review of image-based overall survival prediction in glioma using machine learning
Increasing sample size alone does not guarantee improved accuracy in glioma prediction models, because data quality and feature selection are critical factors. Incorporating diverse imaging modalities can significantly enhance predictive performance. To ensure greater clinical reliability in decision-making, integrating clinical features with imaging data is essential.
Authors
- Fariborz Faeghi (ORCID: https://orcid.org/0000-0002-5132-3577)
- Saman Mohammadpour (ORCID: https://orcid.org/0000-0002-6042-9154)
- Reza Rabiei (ORCID: https://orcid.org/0000-0003-0771-7306)
- Arash Roshanpoor (ORCID: https://orcid.org/0000-0001-6698-715X)
- Hassan Emami (ORCID: https://orcid.org/0000-0003-4309-5049)
- Morteza Tabatabaeefari (ORCID: https://orcid.org/0000-0002-5885-4362)
Institutions
- Islamic Azad University Shahr-e-Rey (IR)
- Shahid Beheshti University of Medical Sciences (IR)
Publication Details
- Journal
- Polish Journal of Radiology
- Published
- 2025-11-27
- DOI
- https://doi.org/10.5114/pjr/209963
- Primary Topic
- Glioma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00