Synopsis
Motivation:
Goals:
Approach:
Results:
452-01-005 / 452-01-005
ISMRM Abstract
Differentiation of Diffuse Midline Glioma from other HGGs using Quantitative Perfusion Parameters and Machine Learning
Accepted
Full abstract & presentation
The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.
Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.
To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.
Log inReferences
1. Louis, D. N. et al. The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol. 131, 803–820 (2016). doi: 10.1007/s00401-016-1545-1. [doi]
2. Louis, D. N. et al. The 2021 WHO classification of tumors of the central nervous system: A summary. Neuro Oncol 23, 1231–1251 (2021). doi: 10.1093/neuonc/noab106. [doi]
3. Zheng, L. et al. Diffuse Midline Gliomas With Histone H3 K27M Mutation in Adults and Children A Retrospective Series of 164 Cases. http://links.lww.com/PAS/ (2022). doi: 10.1097/PAS.0000000000001897. [doi]
4. Park, Y. W. et al. The 2021 WHO classification for gliomas and implications on imaging diagnosis: Part 2—Summary of imaging findings on pediatric-type diffuse high-grade gliomas, pediatric-type diffuse low-grade gliomas, and circumscribed astrocytic gliomas. J. Magn. Reson. Imaging 58, 690–708 (2023). doi: 10.1002/jmri.28740. [doi]
5. Sim, Y. et al. Clinical, qualitative imaging biomarkers, and tumor oxygenation imaging biomarkers for differentiation of midline-located IDH wild-type glioblastomas and H3 K27-altered diffuse midline gliomas in adults. Eur J Radiol 173, (2024). doi: 10.1016/j.ejrad.2024.111384. [doi]
6. Lv, K. et al. Development and validation of a machine learning algorithm for predicting diffuse midline glioma, H3 K27–altered, H3 K27 wild-type high-grade glioma, and primary CNS lymphoma of the brain midline in adults. J Neurosurg 39, 393–401 (2023). doi: 10.3171/2022.11.JNS221544. [doi]
7. Wiśniewski, K., Ghaly, A., Drummond, K. & Fahlström, A. H3 K27M-altered diffuse midline gliomas: a review. Indian J. Neurosurg. 12, 104–115 (2023). doi: 10.1055/s-0043-1771192. [doi]
8. Guo, W. et al. Multiparametric MRI-Based Radiomics Model for Predicting H3 K27M Mutant Status in Diffuse Midline Glioma: A Comparative Study Across Different Sequences and Machine Learning Techniques. Front Oncol 12, (2022). doi: 10.3389/fonc.2022.796583. [doi]
9. Sasi S, D. et al. A comprehensive evaluation and impact of normalization of generalized tracer kinetic model parameters to characterize blood-brain-barrier permeability in normal-appearing and tumor tissue regions of patients with glioma. Magn Reson Imaging 83, 77–88 (2021). doi: 10.1016/j.mri.2021.07.005. [doi]
10. Tofts, P. S. & Kermode, A. G. Measurement of the blood–brain barrier permeability and leakage space using dynamic MR imaging. I. Fundamental concepts. Magn. Reson. Med. 17, 357–367 (1991). doi: 10.1002/mrm.1910170208. [doi]
11. Maurya, S., Yadav, V. K., Agarwal, S. & Singh, A. Brain tumor segmentation in mpMRI scans (BraTS-2021) using models based on U-Net architecture. Int. MICCAI Brainlesion Workshop 312–323 (Springer, Cham, 2021). doi: 10.1007/978-3-031-09002-8_28. [doi]
12. Kesari, A., Maurya, S., Sheikh, M. T., Gupta, R. K. & Singh, A. Large blood vessel segmentation in quantitative DCE-MRI of brain tumors: A Swin UNETR approach. Magn Reson Imaging 118, 110342 (2025). doi: 10.1016/j.mri.2025.110342. [doi]
Cite this abstract
http://echo.ismrm.org/p/ISMRM2026/452-01-005