Optimizing Glioblastoma, IDH-wildtype Treatment Outcomes : A Radiomics and Support Vector Machine-Based Approach to Overall Survival Estimation
The proposed method significantly enhances the predictive accuracy of OS outcomes in GBM, IDH-wildtype patients. By integrating detailed imaging features with key clinical indicators, this model offers a robust tool for personalized treatment planning, potentially improving OS.
Authors
- Priyanka Jain (ORCID: https://orcid.org/0000-0003-2490-893X)
- Sanjay Saxena (ORCID: https://orcid.org/0000-0002-8288-1010)
- Wen‐Cheng Lo (ORCID: https://orcid.org/0000-0003-4880-406X)
- Shivani Prasad
- Jiunn-Kai Chong
- Navneet Kumar Dubey
Institutions
- College of Healthcare Management (JP)
- International Institute of Information Technology (IN)
- Taipei Medical University Hospital (TW)
- International Institute of Information Technology (IN)
- Neuroscience Institute (IT)
- Taipei Medical University (TW)
- Indian Institute of Management Lucknow (IN)
Publication Details
- Journal
- Journal of Korean Neurosurgical Society
- Published
- 2024-08-05
- DOI
- https://doi.org/10.3340/jkns.2024.0100
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.81