Deep learning-based prediction of TERT mutation status from MRI for glioma molecular subtyping
The RegNet model effectively predicts TERT promoter mutation status from routine MRI, offering a non-invasive tool for preoperative molecular subtyping of glioma. This approach may facilitate personalized treatment planning and address limitations of invasive tissue-based diagnostics. Further validation with multi-center data is warranted to enhance clinical applicability.
Authors
- Ruishuang Ma (ORCID: https://orcid.org/0000-0003-4288-9883)
- Yuqing Hu (ORCID: https://orcid.org/0000-0001-8509-2271)
- Qingsong Tao
- Ting Zhu
- Xiaoqin Ge
- Jiangping Ren
- Xuhao Dai
- Jiming Yang
Institutions
- Ningbo University Affiliated Hospital (CN)
- Ningbo First Hospital (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2026-01-29
- DOI
- https://doi.org/10.3389/fneur.2026.1749556
- Primary Topic
- Glioma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00