Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
304-03-004 ISMRM Abstract

Differentiating glioblastoma from solitary brain metastasis using diffusion imaging–based simple, interpretable tumor habitat

Primary: Neuro - Tumors
Secondary: Analysis Methods - Classification and Prediction
304-03-004 · Precision MRI: Advanced Quantitative Imaging and Biomarkers for Brain Tumors · Monday, 11 May, 1:50 PM–3:40 PM · Ballroom East
Keywords: Glioblastoma Neurite orientation dispersion and density imaging Solitary brain metastasis Tumor habitat
Accepted
Guohua Zhao1, Mengyang He2, Xingyu Liu2, Xiaoyue Ma1, Eryuan Gao1, Jing Yan1, Yufei Gao2, MENGZHU WANG3, Yang Gao4, Yong Zhang1
1The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
2Zhengzhou University, Zhengzhou, China
3MR Research Collaboration, Siemens Healthineers Ltd., Beijing, China
4Inner Mongolia Medical University Affiliated Hospital, Hohhot, China
Presenting Author: Jianxun Qu

Synopsis

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References

1. Austin-John, Fordham, Caitlin-Craft, et al. Differentiating Glioblastomas from Solitary Brain Metastases: An Update on the Current Literature of Advanced Imaging Modalities. Cancers. 2021;13(12). doi: 10.3390/cancers13122960. [doi]
2. Kong C, Yan D, Liu K, et al. Multiple deep learning models based on MRI images in discriminating glioblastoma from solitary brain metastases: a multicentre study. BMC Med Imaging 25, 171 (2025). doi: 10.1186/s12880-025-01703-3. [doi]
3. Park YW, Eom S, Kim S, et al. Differentiation of glioblastoma from solitary brain metastasis using deep ensembles: Empirical estimation of uncertainty for clinical reliability. Comput Methods Programs Biomed. 2024; 254:108288. doi: 10.1016/j.cmpb.2024.108288 [doi]
4. Bai J, He M, Gao E, et al. High-performance presurgical differentiation of glioblastoma and metastasis by means of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics. Eur Radiol. 2024;34(10):6616-6628. doi: 10.1007/s00330-024-10686-8. [doi]
5. Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021;18(2):203-211. doi: 10.1038/s41592-020-01008-z. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/304-03-004