An Interpretable Multimodal Machine-Learning Model for Non-Invasive Preoperative Glioma Grading
: A multimodal machine-learning model integrating clinical, structural imaging, and MRS-derived metabolic features was developed and internally validated for non-invasive preoperative glioma grading. The model showed good discrimination and calibration and provided individualized probability estimates, suggesting potential value for preoperative risk stratification. However, clinical deployment remains premature, and further external validation is required.
Authors
- Li Juan Wu (ORCID: https://orcid.org/0000-0002-5468-2487)
- Hao Chen
- Hao Wang (ORCID: https://orcid.org/0000-0001-6881-9977)
- Ye Ding (ORCID: https://orcid.org/0000-0002-8999-4624)
- Xuan Rao (ORCID: https://orcid.org/0009-0003-2077-8776)
- Xianfeng Rao
- M. Yang
- Lei Teng
- Liudong Gong
- Na Zhang
- Guanhao Li
Institutions
- Harbin Medical University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Third Affiliated Hospital of Harbin Medical University (CN)
- Wangjing Hospital of China Academy of Chinese Medical Sciences (CN)
- Second Affiliated Hospital of Harbin Medical University (CN)
- First Affiliated Hospital of Harbin Medical University (CN)
Publication Details
- Journal
- Cancers
- Published
- 2026-04-10
- DOI
- https://doi.org/10.3390/cancers18081204
- Primary Topic
- Glioma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Heilongjiang Province