Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
363-05-004 ISMRM Abstract

Classification of tumor treatment response from multi-time point multisequence MRI scans

Primary: Neuro - Tumors
Secondary: Analysis Methods - Classification and Prediction
363-05-004 · Classification and Analysis in the Brain · Monday, 11 May, 4:10 PM–5:05 PM · Digital Posters Row D
Keywords: Treatment Response T1-Weighted Contrast-Enhanced MRI Balanced accuracy FLAIR MR Multi-timepoint Scan
Accepted
Ewunate A Kassaw1, Satyajit Maurya1, Amit Mehndiratta1,2,3, Anup Singh1,2,4
1Centre for Biomedical Engineering, Indian Institute of Technology, Delhi, India
2Yardi School of Artificial Intelligence, Indian Institute of Technology, Delhi, India
3The University of New South Wales, Sydney, Australia
4Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India
Presenting Author: Puneet Kumar

Synopsis

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References

1. Ostrom, Q. T. et al. The epidemiology of glioma in adults: a “state of the science” review. Neuro-Oncol. 16, 896–913 (2014).
2. Yadav, V. K. et al. Presence of Fragmented Intratumoral Thrombosed Microvasculature in the Necrotic and Peri-Necrotic Regions on SWI Differentiates IDH Wild-Type Glioblastoma From IDH Mutant Grade 4 Astrocytoma. J. Magn. Reson. Imaging 62, 258–270 (2025).
3. Maurya, S., Kumar Yadav, V., Agarwal, S. & Singh, A. Brain Tumor Segmentation in mpMRI Scans (BraTS-2021) Using Models Based on U-Net Architecture. in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (eds Crimi, A. & Bakas, S.) 312–323 (Springer International Publishing, Cham, 2022). doi:10.1007/978-3-031-09002-8_28. [doi]
4. Fernandes, C. et al. Current Standards of Care in Glioblastoma Therapy. Exon Publ. 197–241 (2017) doi:10.15586/codon.glioblastoma.2017.ch11. [doi]
5. Chinot, O. L. et al. Response Assessment Criteria for Glioblastoma: Practical Adaptation and Implementation in Clinical Trials of Antiangiogenic Therapy. Curr. Neurol. Neurosci. Rep. 13, 347 (2013).
6. Kickingereder, P. et al. Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. Lancet Oncol. 20, 728–740 (2019).
7. Suter, Y. et al. Towards Radiomics-Based Automated Disease Progression Assessment for Glioblastoma Patients. in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (eds Baid, U. et al.) 36–47 (Springer Nature Switzerland, Cham, 2024). doi:10.1007/978-3-031-76160-7_4. [doi]
8. Matoso, A. et al. Towards a deep learning approach for classifying treatment response in glioblastomas. Preprint at https://doi.org/10.48550/arXiv.2504.18268 (2025). [doi]
9. Maurya, S., Kassaw, E. A., Sheikh, M. T., Mehndiratta, A. & Singh, A. Automated brain tumor response assessment from longi- tudinal multiparametric MRI data using Swin UNETR and a radiomics based classifier.

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http://echo.ismrm.org/p/ISMRM2026/363-05-004