Machine learning of whole-brain resting-state fMRI signatures for individualized grading of frontal gliomas
Our whole-brain rs-fMRI radiomics approach provides an objective tool for preoperative glioma stratification. The biological interpretability of selected features reflects distinct neuroplasticity patterns between LGGs and HGGs, advancing understanding of glioma-network interactions.
Authors
- Kun Lv (ORCID: https://orcid.org/0000-0003-4967-3727)
- Daoying Geng (ORCID: https://orcid.org/0000-0002-3585-6883)
- Hongyi Chen (ORCID: https://orcid.org/0000-0003-2458-6854)
- Xin Cao (ORCID: https://orcid.org/0000-0003-3839-3076)
- Yue Hu (ORCID: https://orcid.org/0000-0002-0899-5925)
Institutions
- Fudan University (CN)
- Huashan Hospital (CN)
Publication Details
- Journal
- Cancer Imaging
- Published
- 2025-08-04
- DOI
- https://doi.org/10.1186/s40644-025-00920-x
- Primary Topic
- Functional Brain Connectivity Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Fudan University
- Science and Technology Commission of Shanghai Municipality
- Huashan Hospital