Imaging prediction of molecular subtypes in adult-type diffuse glioma considering spatial and temporal heterogeneity
Motivation: Imaging-based molecular prediction is crucial for identifying potential therapeutic targets in adult-type diffuse gliomas. Goal(s): To assess spatial and temporal heterogeneity using diffusion and perfusion MRI and to evaluate isocitrate dehydrogenase (IDH) mutation and epidermal growth factor receptor (EGFR) amplification status in primary and recurrent adult-type diffuse gliomas. Approach: Histogram features and temporally stable imaging features were identified. Models were constructed for IDH and EGFR prediction in cross-sectional and longitudinal sets. Results: The models achieved high performance for predicting IDH mutations, but they exhibited poor predictability in EGFR amplification, especially in recurrent tumors. Impact: This study enables reliable, non-invasive prediction of IDH mutation in gliomas using multiparametric MRI, informing personalized treatment. Caution is advised for EGFR prediction due to its spatial and temporal variability.
Authors
- Sang Woo Song (ORCID: https://orcid.org/0000-0002-5523-3798)
- Seung Chai Jung (ORCID: https://orcid.org/0000-0001-5559-7973)
- Yangsean Choi (ORCID: https://orcid.org/0000-0003-1674-7101)
- Yun Hwa Roh (ORCID: https://orcid.org/0000-0002-8041-1621)
- E‐Nae Cheong (ORCID: https://orcid.org/0000-0003-1270-5385)
- Young‐Hoon Kim (ORCID: https://orcid.org/0000-0002-8852-6503)
- Ji Eun Park (ORCID: https://orcid.org/0000-0002-4419-4682)
- Ho Sung Kim (ORCID: https://orcid.org/0000-0002-9477-7421)
- Jeong Hoon Kim (ORCID: https://orcid.org/0000-0001-7131-4523)
- Chang-Ki Hong (ORCID: https://orcid.org/0000-0002-9987-2559)
Publication Details
- Journal
- Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
- Published
- 2025-09-16
- DOI
- https://doi.org/10.58530/2025/2557
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00