MRI-based radiomic clustering identifies a glioblastoma subtype enriched for neural stemness and proliferative programs
Our findings link a radiomics-defined MRI phenotype to specific molecular programs and cell populations in GBM, suggesting that radiomic subtypes can serve as noninvasive biomarkers of tumor biology and highlighting potential therapeutic targets in aggressive, stem-like tumor cell populations.
Authors
- Xiufu Wu
- Shuyang Wei (ORCID: https://orcid.org/0009-0002-7510-8879)
- Yang Liu (ORCID: https://orcid.org/0000-0002-9368-0425)
- Zhicong Zhang (ORCID: https://orcid.org/0000-0003-2053-7123)
- Youhui Chen
- Zhongyi Zhang (ORCID: https://orcid.org/0000-0002-8758-1949)
- Li Sun (ORCID: https://orcid.org/0000-0001-7950-0953)
- Qian Chen (ORCID: https://orcid.org/0000-0002-8282-2870)
- Shujun Li (ORCID: https://orcid.org/0000-0003-1763-3085)
- Tian Gui (ORCID: https://orcid.org/0009-0004-3300-4646)
Institutions
- Guilin Medical University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-11-25
- DOI
- https://doi.org/10.3389/fonc.2025.1662401
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00