Synopsis
Motivation:
Goals:
Approach:
Results:
469-06-012
ISMRM Abstract
MRI Radiomics for Survival Risk Stratification of Molecular Glioblastoma Defined by the 2021 WHO Classification
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 DN, Perry A, Wesseling P, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol. 2021;23(8):1231-1251. doi:10.1093/neuonc/noab106 [doi]
2. Gupta M, Anjari M, Brandner S, et al. Isocitrate Dehydrogenase 1/2 Wildtype Adult Astrocytoma with WHO Grade 2/3 Histological Features: Molecular Re-Classification, Prognostic Factors, Clinical Outcomes. Biomedicines. 2024;12(4)doi:10.3390/biomedicines12040901 [doi]
3. Lin AL, Rosenblum M, Mellinghoff IK, et al. Prognostic and radiographic correlates of a prospectively collected molecularly profiled cohort of IDH1/2-wildtype astrocytomas. Brain Pathol. 2020;30(3):653-660. doi:10.1111/bpa.12826 [doi]
4. Nakasu S, Deguchi S, Nakasu Y. IDH wild-type lower-grade gliomas with glioblastoma molecular features: a systematic review and meta-analysis. Brain Tumor Pathol. 2023;40(3):143-157. doi:10.1007/s10014-023-00463-8 [doi]
5. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2016;278(2):563-77. doi:10.1148/radiol.2015151169 [doi]
6. Chen L, Chen R, Li T, Tang C, Li Y, Zeng Z. Multi-parameter MRI based radiomics nomogram for predicting telomerase reverse transcriptase promoter mutation and prognosis in glioblastoma. Front Neurol. 2023;14:1266658. doi:10.3389/fneur.2023.1266658 [doi]
7. Zhang H, Zhang H, Zhang Y, et al. Multiparametric MRI-based fusion radiomics for predicting telomerase reverse transcriptase (TERT) promoter mutations and progression-free survival in glioblastoma: a multicentre study. Neuroradiology. 2024;66(1):81-92. doi:10.1007/s00234-023-03245-3 [doi]
8. Chong JK, Jain P, Prasad S, Dubey NK, Saxena S, Lo WC. Optimizing Glioblastoma, IDH-wildtype Treatment Outcomes : A Radiomics and Support Vector Machine -Based Approach to Overall Survival Estimation. J Korean Neurosurg Soc. 2024;doi:10.3340/jkns.2024.0100 [doi]
9. Yushkevich PA, Yang G, Gerig G. ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images. Annu Int Conf IEEE Eng Med Biol Soc. 2016;2016:3342-3345. doi:10.1109/embc.2016.7591443 [doi]
10. Song Y, Zhang J, Zhang YD, et al. FeAture Explorer (FAE): A tool for developing and comparing radiomics models. PLoS One. 2020;15(8):e0237587. doi:10.1371/journal.pone.0237587 [doi]
11. Poursaeed R, Mohammadzadeh M, Safaei AA. Survival prediction of glioblastoma patients using machine learning and deep learning: a systematic review. BMC Cancer. 2024;24(1):1581. doi:10.1186/s12885-024-13320-4 [doi]
12. Choi Y, Nam Y, Jang J, et al. Radiomics may increase the prognostic value for survival in glioblastoma patients when combined with conventional clinical and genetic prognostic models. Eur Radiol. 2021;31(4):2084-2093. doi:10.1007/s00330-020-07335-1 [doi]
13. Bakas S, Shukla G, Akbari H, et al. Overall survival prediction in glioblastoma patients using structural magnetic resonance imaging (MRI): advanced radiomic features may compensate for lack of advanced MRI modalities. J Med Imaging (Bellingham). 2020;7(3):031505. doi:10.1117/1.Jmi.7.3.031505 [doi]
14. Jajroudi M, Enferadi M, Homayoun AA, Reiazi R. MRI-based machine learning for determining quantitative and qualitative characteristics affecting the survival of glioblastoma multiforme. Magn Reson Imaging. 2022;85:222-227. doi:10.1016/j.mri.2021.10.023 [doi]
15. Cepeda S, Pérez-Nuñez A, García-García S, et al. Predicting Short-Term Survival after Gross Total or Near Total Resection in Glioblastomas by Machine Learning-Based Radiomic Analysis of Preoperative MRI. Cancers (Basel). 2021;13(20)doi:10.3390/cancers13205047 [doi]
16. Chen X, Fang M, Dong D, et al. Development and Validation of a MRI-Based Radiomics Prognostic Classifier in Patients with Primary Glioblastoma Multiforme. Acad Radiol. 2019;26(10):1292-1300. doi:10.1016/j.acra.2018.12.016 [doi]
17. Bae S, Choi YS, Ahn SS, et al. Radiomic MRI Phenotyping of Glioblastoma: Improving Survival Prediction. Radiology. 2018;289(3):797-806. doi:10.1148/radiol.2018180200 [doi]
18. Kim Y, Kim KH, Park J, Yoon HI, Sung W. Prognosis prediction for glioblastoma multiforme patients using machine learning approaches: Development of the clinically applicable model. Radiother Oncol. 2023;183:109617. doi:10.1016/j.radonc.2023.109617 [doi]
19. Kickingereder P, Neuberger U, Bonekamp D, et al. Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma. Neuro Oncol. 2018;20(6):848-857. doi:10.1093/neuonc/nox188 [doi]
Cite this abstract
http://echo.ismrm.org/p/ISMRM2026/469-06-012