Development of a multimodal model combining radiomics and deep learning to predict malignant cerebral edema after endovascular thrombectomy
This study integrated clinical data, radiomics, and DL features to develop a multimodal predictive model with a strong discriminative ability to predict MCE after EVT.
Authors
- Feifan Liu (ORCID: https://orcid.org/0000-0003-0881-6365)
- Jianhui Fu (ORCID: https://orcid.org/0000-0002-4594-2451)
- Yan Li (ORCID: https://orcid.org/0000-0002-9481-8086)
- Yuhan Chen (ORCID: https://orcid.org/0000-0003-0695-0317)
- Jingjing Fu
- Yujie Shen
- Jiayi Hong
- Sheng Hu (ORCID: https://orcid.org/0000-0002-2296-0243)
Institutions
- Zhejiang University (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-11-27
- DOI
- https://doi.org/10.3389/fneur.2025.1716443
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Medical Scientific Research Foundation of Zhejiang Province, China
- Fundamental Research Funds for the Central Universities