Multimodal radiomics of cerebellar subregions for machine learning-driven Alzheimer’s disease diagnosis
The multimodal radiomics model based on cerebellar subregions, which integrates [<sup>18</sup>F]FDG PET and 3DT1W MRI data, can effectively diagnose AD and provide potential biomarkers for clinical applications.
Authors
- Xiulin Wang (ORCID: https://orcid.org/0000-0002-8884-0973)
- Ruichen Liu (ORCID: https://orcid.org/0000-0002-1631-9652)
- Chunbo Dong (ORCID: https://orcid.org/0000-0003-1045-6768)
- Changjun Ma
- Yang Jiao
- Xinqing Hao
- Ying Li (ORCID: https://orcid.org/0000-0001-5319-3094)
- Jing Liu (ORCID: https://orcid.org/0009-0008-2772-0418)
Institutions
- Dalian Medical University (CN)
- First Affiliated Hospital of Dalian Medical University (CN)
Publication Details
- Journal
- Frontiers in Aging Neuroscience
- Published
- 2025-10-27
- DOI
- https://doi.org/10.3389/fnagi.2025.1679788
- Primary Topic
- Advanced Neuroimaging Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Foundation for the National Institutes of Health
- Alzheimer's Association
- Bristol-Myers Squibb
- Eli Lilly and Company
- Biogen
- Alzheimer's Disease Neuroimaging Initiative
- BioClinica
- Northern California Institute for Research and Education
- Eisai
- National Institutes of Health
- Canadian Institutes of Health Research