Development and validation of a preoperative magnetic resonance imaging-based and machine learning model for the noninvasive differentiation of intracranial glioblastoma, primary central nervous system lymphoma and brain metastases: a retrospective a
The SVM-based MRI radiomics model effectively distinguishes between GB, PCNSL, and BM. Combining CE-T1 and T2 sequences significantly enhances classification performance, providing a robust, noninvasive diagnostic tool that could assist in treatment planning and improve patient outcomes.
Authors
- Yu Zhang (ORCID: https://orcid.org/0000-0001-7455-9016)
- Dongsheng Kong
- Yuxiang Sun (ORCID: https://orcid.org/0009-0002-0915-6557)
- Liqin Wei (ORCID: https://orcid.org/0000-0002-5525-9752)
- Chunhui Li (ORCID: https://orcid.org/0000-0003-2186-5137)
- Junpeng Xu (ORCID: https://orcid.org/0000-0003-0485-3644)
- Zihao Zhu (ORCID: https://orcid.org/0000-0003-2768-8477)
- Shiyu Feng (ORCID: https://orcid.org/0009-0001-3060-541X)
- Qijia Wu
Institutions
- Chinese PLA General Hospital (CN)
- Beijing Xuanwu Traditional Chinese Medicine Hospital (CN)
- Affiliated Hospital of Hebei University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-04-22
- DOI
- https://doi.org/10.3389/fonc.2025.1541350
- Citations
- 1
- Primary Topic
- Glioma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.63