Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
364-05-003 ISMRM Abstract

A structural assessment of glioma subtypes comparing non-Gaussian and Gaussian diffusion metrics for classification

Primary: Neuro - Tumors
Secondary: Diffusion - DWI/DTI/DKI
364-05-003 · Classification in Diffusion MRI I · Monday, 11 May, 4:10 PM–5:05 PM · Digital Posters Row E
Keywords: Glioma Non Gaussian Diffusion Molecular Classification Diffusion Tensor Imaging (DTI)
Accepted
Franklyn Howe 1, Ian Storey1, Timothy Jones1,2, Christopher Murphy1,3, Philip Benjamin1,4, Thomas R Barrick1
1Dept Psychology & Neuroscience, City St George's, London, United Kingdom
2Dept Neurosurgery, St George's Hospital University Trust, London, United Kingdom
3Dept Radiology, St George's Hospital University Trust, London, United Kingdom
4Dept Neuroradiology, St George's Hospital University Trust, London, United Kingdom
Presenting Author: Franklyn Howe

Synopsis

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References

1. Horbinski C, Berger T, Packer RJ and Wen PY (2022). Clinical implications of the 2021 edition of the WHO classification of central nervous system tumours. Nature Revews Neurology 18: 515. https://doi.org/10.1038/ s41582-022-00679-w [doi]
2. Zhao H, Hou Z, He Q, Liu X and Xie J (2025). The diagnostic and prediction performance of MR diffusion kurtosis imaging in the glioma molecular classification: a systematic review and meta-analysis. Frontiers Neurology 16:1543619. doi: 10.3389/fneur.2025.1543619 [doi]
3. Zheng F, Zhang L, Chen H, Zang Y, Chen X and Li Y (2024). Radiomics for predicting MGMT status in cerebral glioblastoma: comparison of different MRI sequences. Journal of Radiation Research 65: 350–35. https://doi.org/10.1093/jrr/rrae007 [doi]
4. Doniselli FM, Pascuzzo R, Mazzi F, Padelli F, Moscatelli M, Akinci D’Antonoli TA, Cuocolo R, Aquino A, Cuccarini V and Sconfienza L (2024). Quality assessment of the MRI-radiomics studies for MGMT promoter methylation prediction in glioma: a systematic review and meta-analysis. European Radiology 34:5802-5815. https://doi.org/10.1007/s00330-024-10594-x [doi]
5. Chung CYC and Pigott LE (2024). Predicting IDH and ATRX mutations in gliomas from radiomic features with machine learning: a systematic review and meta-analysis. Frontiers Radiology 4:1493824. doi: 10.3389/fradi.2024.1493824 [doi]
6. Hempel J-M, Bisdas S, Schittenhelm J, Brendle C, Bender B, Wassmann H, Skardelly M, Tabatabai G, Vega SC, Ernemann U, Klose U (2017). In vivo molecular profiling of human glioma using diffusion kurtosis imaging. J Neurooncol 131:93–101. DOI 10.1007/s11060-016-2272-0 [doi]
7. Nuessle NC, Behling F. Tabatabai G, Castaneda Vega S, Schittenhelm J, Ernemann U, Klose U, Hempel J-M (2021). ADC-Based Stratification of Molecular Glioma Subtypes Using High b-Value Diffusion-Weighted Imaging. Journal Clinical Medicine 10:3451. https://doi.org/10.3390/jcm10163451 [doi]
8. Barrick TR, Spilling CA, Ingo C, Madigan J, Isaacs JD, Rich P, Jones TL, Magin RL, Hall MG, Howe FA (2020). Quasi-diffusion magnetic resonance imaging (QDI): A fast, high b-value diffusion imaging technique. NeuroImage 211:116606. https://doi.org/10.1016/j.neuroimage.2020.116606 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/364-05-003