One class classification-empowered radiomics for noninvasively accurate prediction of glioma isocitrate dehydrogenase mutation using multiparametric magnetic resonance imaging
This study demonstrates the feasibility of integrating one-class classification into radiomics for the determination of preoperative IDH mutation status in patients with glioma using multiparametric MRI. This versatile model holds potential for other diseases with substantial data imbalance.
Authors
- Songtao Ding (ORCID: https://orcid.org/0000-0001-6640-2190)
- Wen Zheng (ORCID: https://orcid.org/0000-0002-1098-0490)
- Chao Gong (ORCID: https://orcid.org/0000-0001-7196-5588)
- Xu Gong (ORCID: https://orcid.org/0000-0001-6383-0667)
- Xiaoping P. Wang (ORCID: https://orcid.org/0000-0001-7143-8112)
- Liang Xu (ORCID: https://orcid.org/0000-0002-4261-2887)
- Jian Jia (ORCID: https://orcid.org/0000-0002-8648-0564)
- Xingxing Yuan (ORCID: https://orcid.org/0000-0002-9894-4127)
- Yun Zhang (ORCID: https://orcid.org/0000-0001-9457-7801)
Institutions
- Jiangxi Provincial Cancer Hospital (CN)
Publication Details
- Journal
- Clinical Radiology
- Published
- 2025-03-06
- DOI
- https://doi.org/10.1016/j.crad.2025.106866
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36