Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
303-03-002 ISMRM Abstract

MRI-based deep learning system for noninvasive neuropathological profiling of adult-type diffuse glioma

Accepted
Yangyang Li1, Xiaoming Hong2, Chenghao Liu2, Junjie Li1, Haowen Pang2, Siyao Xu3, Renlong Zhang4, xianchang zhang5, Zhizheng Zhuo3, Chuyang Ye2, Yaou Liu3,6
1Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
2School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China
3Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
4Neusoft Medical Systems Co., Ltd., Hangzhou, China
5MR Research Collaboration, Siemens Healthineers Ltd., Beijing, China
6China National Clinical Research Center for Neurological Diseases, Beijing, China
Presenting Author: Shan Lv

Synopsis

Motivation:
Goals:
Approach:
Results:
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 in

References

1. Ostrom QT, Price M, Neff C, et al. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2016—2020. Neuro-Oncology 2023;25(Supplement_4):iv1-iv99. doi: 10.1093/neuonc/noad149 [doi]
2. Berger TR, Wen PY, Lang-Orsini M, et al. World Health Organization 2021 Classification of Central Nervous System Tumors and Implications for Therapy for Adult-Type Gliomas. JAMA Oncology 2022;8(10) doi: 10.1001/jamaoncol.2022.2844 [doi]
3. Weller M, van den Bent M, Preusser M, et al. EANO guidelines on the diagnosis and treatment of diffuse gliomas of adulthood. Nature Reviews Clinical Oncology 2020;18(3):170-86. doi: 10.1038/s41571-020-00447-z [doi]
4. Patel T, Bander ED, Venn RA, et al. The Role of Extent of Resection in IDH1 Wild-Type or Mutant Low-Grade Gliomas. Neurosurgery 2018;82(6):808-14. doi: 10.1093/neuros/nyx265 [doi]
5. Garton ALA, Kinslow CJ, Rae AI, et al. Extent of resection, molecular signature, and survival in 1p19q-codeleted gliomas. Journal of Neurosurgery 2021;134(5):1357-67. doi: 10.3171/2020.2.Jns192767 [doi]
6. Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with Deep Neural Networks. Medical Image Analysis 2017;35:18-31. doi: 10.1016/j.media.2016.05.004 [doi]
7. Khalighi S, Reddy K, Midya A, et al. Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment. npj Precision Oncology 2024;8(1) doi: 10.1038/s41698-024-00575-0 [doi]
8. van der Voort SR, Incekara F, Wijnenga MMJ, et al. Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning. Neuro-Oncology 2023;25(2):279-89. doi: 10.1093/neuonc/noac166 [doi]
9. Sun X, Pang P, Lou L, et al. Radiomic prediction models for the level of Ki-67 and p53 in glioma. Journal of International Medical Research 2020;48(5) doi: 10.1177/0300060520914466 [doi]
10. Choi YS, Bae S, Chang JH, et al. Fully automated hybrid approach to predict theIDHmutation status of gliomas via deep learning and radiomics. Neuro-Oncology 2021;23(2):304-13. doi: 10.1093/neuonc/noaa177 [doi]
11. Han Y, Xie Z, Zang Y, et al. Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas. Journal of Neuro-Oncology 2018;140(2):297-306. doi: 10.1007/s11060-018-2953-y [doi]
12. Zeiler MD, Fergus R. Visualizing and Understanding Convolutional Neural Networks. Springer International Publishing 2013
13. Pang H, Guo W, Ye C. Multi-modal brain MRI synthesis based on SwinUNETR. 2025
14. Wu X, Zhang S, Zhang Z, et al. Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patients. npj Precision Oncology 2024;8(1) doi: 10.1038/s41698-024-00670-2 [doi]
15. Zhu Z, Wang H, Li T, et al. OMT and tensor SVD–based deep learning model for segmentation and predicting genetic markers of glioma: A multicenter study. Proceedings of the National Academy of Sciences 2025;122(28) doi: 10.1073/pnas.2500004122 [doi]
16. Chakrabarty S, LaMontagne P, Shimony J, et al. MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network. Neuro-Oncology Advances 2023;5(1) doi: 10.1093/noajnl/vdad023 [doi]

Cite this abstract