Noninvasive glioma stratification by multiparametric metabolic and microstructural MRI at 3T
Motivation: Reliable, noninvasive identification of glioma subtypes is essential to optimize treatment and improve patient care. Goal(s): To identify glioma subtypes according to the 2021 WHO classification using a multiparametric MRI and MRS approach. Approach: Three-dimensional Principal Component Analysis was employed to analyze in vivo edited MRS, tensor-valued diffusion MRI, and APTw 3T data in a cohort of 35 patients with a newly diagnosed glioma. Results: Diffusion MRI differentiated grade 2 astrocytomas from other glioma types with high accuracy, while APTw imaging helped separating low- and high-grade gliomas. MRS identified IDH-mutants by D-2-hydroxyglutarate and oligodendrogliomas by cystathionine levels. Impact: Characterization of novel biomarkers of glioma metabolism and microstructure could enhance diagnostics, deepen understanding of glioma biology, and support an improved glioma stratification. Our PCA model demonstrated high accuracy in distinguishing glioma subtypes defined by WHO 2021 histomolecular classification.
Authors
- Stéphane Lehéricy (ORCID: https://orcid.org/0000-0002-5802-3518)
- Markus Nilsson (ORCID: https://orcid.org/0000-0002-3140-8223)
- Franck Bielle (ORCID: https://orcid.org/0000-0001-6564-6388)
- Małgorzata Marjańska (ORCID: https://orcid.org/0000-0002-4727-2447)
- Moritz Zaiß (ORCID: https://orcid.org/0000-0001-9780-3616)
- Francesca Branzoli (ORCID: https://orcid.org/0000-0001-9792-0492)
- Filip Szczepankiewicz (ORCID: https://orcid.org/0000-0002-5251-587X)
- Marc Sanson (ORCID: https://orcid.org/0000-0002-1813-8476)
- Lucia Nichelli (ORCID: https://orcid.org/0009-0006-0934-5365)
- Stefano Casagranda (ORCID: https://orcid.org/0000-0002-8705-9464)
- Bertrand Mathon (ORCID: https://orcid.org/0000-0002-9182-5846)
- François‐Xavier Lejeune (ORCID: https://orcid.org/0000-0002-6229-5364)
- Mélanie Didier
- Capucine Cadin
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/10.58530/2025/2397
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00