Machine learning-based preoperative prediction of <i>IDH</i> mutation status in adult gliomas using clinical features
This validated AdaBoost model offers a practical and interpretable solution for preoperative IDH status assessment, demonstrating both diagnostic reliability and biological plausibility through its feature importance profile.
Authors
- Guo‐Hao Huang (ORCID: https://orcid.org/0000-0002-6687-5673)
- Shengqing Lv (ORCID: https://orcid.org/0000-0003-3574-2779)
- Fei Li (ORCID: https://orcid.org/0000-0001-7279-7897)
- Wei Chen (ORCID: https://orcid.org/0009-0000-0883-2547)
- Cheng Yang
- Jingpeng Liu (ORCID: https://orcid.org/0000-0002-0112-879X)
- Peng Ren (ORCID: https://orcid.org/0009-0005-1409-9472)
Institutions
- Army Medical University (CN)
Publication Details
- Journal
- Neurological Research
- Published
- 2025-07-13
- DOI
- https://doi.org/10.1080/01616412.2025.2533418
- Primary Topic
- Glioma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China