Development of a hybrid 2.5D deep learning model for glioma survival prediction using T1-weighted MRI from the CGGA database
The 2.5D multi-source framework provides a clinically feasible, non-invasive tool for preoperative survival prediction, enabling personalized therapeutic strategies for glioma patients.
Authors
- Liang Xia (ORCID: https://orcid.org/0000-0002-5439-3773)
- Ke Jin (ORCID: https://orcid.org/0000-0001-7697-0466)
- Caixing Sun (ORCID: https://orcid.org/0000-0002-3270-5975)
Institutions
- Zhejiang Cancer Hospital (CN)
Publication Details
- Journal
- European Journal of Radiology Open
- Published
- 2025-10-26
- DOI
- https://doi.org/10.1016/j.ejro.2025.100697
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00