Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
469-06-008 ISMRM Abstract

Can VASARI Serve as a Lightweight MRI Tool for Comprehensive Prediction of Adult-Type Diffuse Glioma Under WHO CNS5?

Primary: Neuro - Tumors
Secondary: Analysis Methods - Classification and Prediction
469-06-008 · Radiomics: Applications · Tuesday, 12 May, 4:55 PM–5:50 PM · Digital Posters Row J
Keywords: Radiomics Machine learning VASARI features Adult-type diffuse glioma
Accepted
Jiaxin Lin 1, Fangrong Liang1, Xiaoyi Huang2, Yujie He1, Yongzhou Xu3, Ruimeng Yang1
1Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China
2Guangzhou First People’s Hospital, Guangdong Medical University, Guangzhou，Guangdong, China
3Clinical and Technical Support, Philips Healthcare (Guangzhou), Guangzhou, China
Presenting Author: Jiaxin Lin

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References

1. Whitfield BT, Huse JT. Classification of adult‐type diffuse gliomas: Impact of the World Health Organization 2021 update. Brain Pathology. 2022; 32(4).
2. Weller M, Wen PY, Chang SM, et al. Glioma. Nature Reviews Disease Primers. 2024; 10(1).
3. Long C, Xu D, Sun W, et al. Nomogram prediction of molecular characteristics in WHO grade 3–4 diffuse gliomas based on fractal analysis and VASARI features. Scientific Reports. 2025; 15(1).
4. Wang P, Xie S, Wu Q, et al. Model incorporating multiple diffusion MRI features: development and validation of a radiomics-based model to predict adult-type diffuse gliomas grade. European Radiology. 2023; 33(12):8809-20.
5. Wang P, He J, Ma X, et al. Applying MAP-MRI to Identify the WHO Grade and Main Genetic Features of Adult-type Diffuse Gliomas: A Comparison of Three Diffusion-weighted MRI Models. Academic Radiology. 2023; 30(7):1238-46.
6. Du N, Shu W, Li K, et al. An initial study on the predictive value using multiple MRI characteristics for Ki-67 labeling index in glioma. Journal of Translational Medicine. 2023; 21(1).
7. Khorasani A, Dadashi serej N, jalilian M, Shayganfar A, Tavakoli MB. Performance comparison of different medical image fusion algorithms for clinical glioma grade classification with advanced magnetic resonance imaging (MRI). Scientific Reports. 2023; 13(1).
8. Doniselli FM, Pascuzzo R, Mazzi F, et al. Quality assessment of the MRI-radiomics studies for MGMT promoter methylation prediction in glioma: a systematic review and meta-analysis. European Radiology. 2024; 34(9):5802-15.
9. Liu X, Zhang Q, Li J, et al. Coordinatized lesion location analysis empowering ROI-based radiomics diagnosis on brain gliomas. European Radiology. 2023; 33(12):8776-87.
10. Xie X, Luo C, Wu S, et al. Recursive partitioning analysis for survival stratification and early imaging prediction of molecular biomarker in glioma patients. BMC Cancer. 2024; 24(1).
11. Li L, Fu Y, Zhang Y, et al. Magnetic resonance imaging findings of intracranial extraventricular ependymoma: A retrospective multi‐center cohort study of 114 cases. Cancer Medicine. 2023; 12(15):16195-206.
12. Wegscheid ML, Jennings JW. VASARI Glioma Features: Insights into Their Impact and Performance. Radiology: Imaging Cancer. 2024; 6(5).
13. Brancato V, Cerrone M, Lavitrano M, Salvatore M, Cavaliere C. A Systematic Review of the Current Status and Quality of Radiomics for Glioma Differential Diagnosis. Cancers. 2022; 14(11).
14. Wang Z, Wang L, Wang Y. Radiomics in glioma: emerging trends and challenges. Annals of Clinical and Translational Neurology. 2025; 12(3):460-77.
15. Dedhia M, Germano IM. The Evolving Landscape of Radiomics in Gliomas: Insights into Diagnosis, Prognosis, and Research Trends. Cancers. 2025; 17(9).
16. Han Y, Wang Y-y, Yang Y, et al. Association between dichotomized VASARI feature and overall survival in glioblastoma patients: a single-institution propensity score matching analysis. Cancer Imaging. 2024; 24(1).

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/469-06-008