Glioblastoma survival prediction through MRI and clinical data integration with transfer learning
Integrating encoder-derived features from multimodal MRI data with clinical variables offers a scalable and effective approach for OS prediction in glioblastoma patients. This study demonstrates the potential of deep learning to address traditional radiomics limitations, paving the way for more precise and personalized prognostic tools.
Authors
- Fabio Martino Doniselli (ORCID: https://orcid.org/0000-0002-2272-7855)
- Riccardo Pascuzzo (ORCID: https://orcid.org/0000-0001-6555-1784)
- Alessandra Marasi (ORCID: https://orcid.org/0000-0001-7992-5222)
- Marina Grisoli (ORCID: https://orcid.org/0000-0002-7663-446X)
- Domenico Aquino (ORCID: https://orcid.org/0000-0002-1289-9573)
- Alberto Redaelli (ORCID: https://orcid.org/0000-0002-9020-2188)
- Elena De Momi (ORCID: https://orcid.org/0000-0002-8819-2734)
- Davide Milesi
Institutions
- Fondazione IRCCS Istituto Neurologico Carlo Besta (IT)
- Politecnico di Milano (IT)
Publication Details
- Journal
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2025-12-04
- DOI
- https://doi.org/10.1007/s11548-025-03548-1
- Primary Topic
- Glioma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministero della Salute