Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
363-05-002 ISMRM Abstract

Comparative Analysis of Radiologist, Radiomics, and Multidimensional Deep Learning for Preoperative Molecular Classification

Primary: Neuro - Tumors
Secondary: Analysis Methods - Classification and Prediction
363-05-002 · Classification and Analysis in the Brain · Monday, 11 May, 4:10 PM–5:05 PM · Digital Posters Row D
Keywords: Quantitative MRI Deep learning Glioma classification Adult Diffuse Gliomas Molecular Classification
Accepted
Xin Ge 1,2, Yuhui Xiong3, Jing Zhang4, Wen Wang1,2
1Department of Radiology & Functional and Molecular Imaging Key Lab of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
27T MRI Precision Neurology Platform of Shaanxi Province, Tangdu Hospital, Fourth Military Medical University, Xi'an, China
3GE HealthCare MR Research, Beijing, China
4The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, China
Presenting Author: Xin Ge

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References

1. Miller JJ, Gonzalez Castro LN, McBrayer S, et al. Isocitrate dehydrogenase (IDH) mutant gliomas: A Society for Neuro-Oncology (SNO) consensus review on diagnosis, management, and future directions. Neuro Oncol. 2023;25(1):4-25.
2. 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.
3. Zhou Z. Artificial intelligence on MRI for molecular subtyping of diffuse gliomas: feature comparison, visualization, and correlation between radiomics and deep learning. Eur Radiol. 2022;32(2):745-746.
4. Ge X, Wang M, Ma H, et al. Investigated diagnostic value of synthetic relaxometry, three-dimensional pseudo-continuous arterial spin labelling and diffusion-weighted imaging in the grading of glioma. Magn Reson Imaging. 2022;86:20-27.
5. Ge X, Gan T, Yang Z, et al. Whole-tumor histogram analysis of synthetic magnetic resonance imaging predicts isocitrate dehydrogenase mutation status in gliomas. Quant Imaging Med Surg. 2024;14(3):2225-2239.
6. Liu Y, Zheng Z, Wang Z, et al. Using radiomics based on multicenter magnetic resonance images to predict isocitrate dehydrogenase mutation status of gliomas. Quant Imaging Med Surg. 2023;13(4):2143-2155.
7. Li Y, Wei D, Liu X, et al. Molecular subtyping of diffuse gliomas using magnetic resonance imaging: comparison and correlation between radiomics and deep learning. Eur Radiol. 2022;32(2):747-758.
8. Alleman K, Knecht E, Huang J, Zhang L, Lam S, DeCuypere M. Multimodal Deep Learning-Based Prognostication in Glioma Patients: A Systematic Review. Cancers (Basel). 2023;15(2):545.
9. Kim H, Lee D, Cho WS, et al. CT-based deep learning model to differentiate invasive pulmonary adenocarcinomas appearing as subsolid nodules among surgical candidates: comparison of the diagnostic performance with a size-based logistic model and radiologists. Eur Radiol. 2020;30(6):3295-3305.
10. Niu W, Yan J, Hao M, et al. MRI transformer deep learning and radiomics for predicting IDH wild type TERT promoter mutant gliomas. NPJ Precis Oncol. 2025;9(1):89.

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http://echo.ismrm.org/p/ISMRM2026/363-05-002