Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
469-06-007 ISMRM Abstract

Multimodal Radiomics and Machine Learning for Preoperative Classification of Meningioma Grade: Multi algorithm Comparison and

Primary: Neuro
Secondary: Analysis Methods
469-06-007 · Radiomics: Applications · Tuesday, 12 May, 4:55 PM–5:50 PM · Digital Posters Row J
Keywords: Meningiomas Machine learning (ML) Grade
Accepted
Yuxia Liang 1, Jin wang1, Qinqin Xie1, Haining Li2, Yu Shang3, Ming Zhang1,4,5, chen niu1,2
1The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
2Department of PET/CT, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
3Xi'an Jiaotong tniversity, China
4School of Future Technology, Xi'an Jiaotong University, Xi'an, China
5Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
Presenting Author: Yuxia Liang

Synopsis

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References

1. Wang JZ, Landry AP, Raleigh DR, et al. Meningioma: International Consortium on Meningiomas consensus review on scientific advances and treatment paradigms for clinicians, researchers, and patients. Neuro Oncol. 2024 Oct 3;26(10):1742-1780. doi: 10.1093/neuonc/noae082. [doi]
2. Zhao Z, Nie C, Zhao L, et al. Multi-parametric MRI-based machine learning model for prediction of WHO grading in patients with meningiomas. Eur Radiol. 2024 Apr;34(4):2468-2479. doi: 10.1007/s00330-023-10252-8. [doi]
3. Schwier M, van Griethuysen J, Vangel MG, et al. Repeatability of multiparametric prostate MRI radiomics features. Sci Rep. 2019; 9:9441. doi: 10.1038/s41598-019-45766-z. [doi]
4. Chen J, Xue Y, Ren L, et al. Predicting meningioma grades and pathologic marker expression via deep learning. Eur Radiol. 2024 May;34(5):2997-3008. Epub 2023 Oct 19. doi: 10.1007/s00330-023-10258-2. [doi]
5. Caulfield S, Menezes G, Marignol L, et al. Nomograms are key decision-making tools in prostate cancer radiation therapy. Urologic Oncology: Seminars and Original Investigations. Elsevier, 2018, 36(6): 283-292. doi: 10.1016/j.urolonc.2018.03.017. Epub 2018 Apr 19. [doi]
6. Balachandran V P, Gonen M, Smith J J, et al. Nomograms in oncology: more than meets the eye. The lancet oncology, 2015, 16(4): e173-e180. doi: 10.1016/S1470-2045(14)71116-7. [doi]
7. Goldbrunner R, Stavrinou P, Jenkinson MD, et al. EANO guideline on the diagnosis and management of meningiomas. Neuro Oncol. 2021 Nov 2;23(11):1821-1834. doi: 10.1093/neuonc/noab150. [doi]
8. Wu W, Ding Y, Su Y, et al. Novel MRI signs for differentiating neurogenic and non-neurogenic peripheral nerve Tumors: Insights from Contrast-Enhanced magnetic resonance neurography. European Journal of Radiology, 2025, 183: 111894. doi: 10.1016/j.ejrad.2024.111894. Epub 2024 Dec 18. [doi]
9. Joo L, Park JE, Park SY, et al. Extensive peritumoral edema and brain-to-tumor interface MRI features enable prediction of brain invasion in meningioma: development and validation. Neuro Oncol. 2021 Feb 25;23(2):324-333. doi: 10.1093/neuonc/noaa190. [doi]
10. Wang JZ, Landry AP, Raleigh DR, et al. Meningioma: International Consortium on Meningiomas consensus review on scientific advances and treatment paradigms for clinicians, researchers, and patients. Neuro Oncol. 2024 Oct 3;26(10):1742-1780. doi: 10.1093/neuonc/noae082. [doi]

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http://echo.ismrm.org/p/ISMRM2026/469-06-007