Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
566-04-010 ISMRM Abstract

Leveraging Adult Brain Tumour AI Models to Predict Paediatric Neuro-Oncology Outcomes and Survival

Primary: Pediatrics - Neuro
Secondary: Analysis Methods - Segmentation and Detection
566-04-010 · Predicting Neurologic Outcomes · Wednesday, 13 May, 2:35 PM–3:30 PM · Digital Posters Row G
Keywords: Brain Tumor Segmentation Paediatric neuroimaging Survival analysis
Accepted
Tolou Shadbahr1, James K Ruffle2,3, Liam Burrows4, Ivano Palladino5, Jenny Gains6, Michael Roberts4, Harpreet Hyare 7
1University of Helsinki, Helsinki, Finland
2Queen Square Institute of Neurology, University College London, London, United Kingdom
3Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, United Kingdom
4Cambridge University department, Cambridge, United Kingdom
5University College, London, United Kingdom
6University College London Hospitals NHS Foundation Trust, London, United Kingdom
7Department of Translational Neuroscience and Stroke, University College London, London, United Kingdom
Presenting Author: Harpreet Hyare

Synopsis

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References

1. Menze BH et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans Med Imaging. 2015;34(10):1993–2024. PMID: 25494501. doi:10.1109/TMI.2014.2377694 [doi] [pmid]
2. Khan S, Islam N, Jan Z, Din IU, Rodrigues JJPC. A review on deep learning for brain tumor diagnosis and classification. IEEE Access. 2021;9:162949–162965. doi:10.1109/ACCESS.2021.3133236 [doi]
3. Ruffle JK et al. Deep learning segmentation of gliomas in clinical routine MRI: a generalisable approach. Brain Commun. 2023;5(3):fcad134. PMID: 37622512. doi:10.1093/braincomms/fcad134 [doi] [pmid]
4. Thiebaut de Schotten M et al. Disconnectome mapping reveals structural disconnection in multiple brain disorders. Nat Commun. 2020;11(1):5248. PMID: 33051424. doi:10.1038/s41467-020-18920-9 [doi] [pmid]
5. Ruffle JK et al. Latent feature extraction using autoencoders for neuro-oncological outcome prediction. BNOS 2025, Abstract #163.
6. Chang P et al. Deep-learning convolutional neural networks accurately classify genetic mutations in gliomas. AJNR Am J Neuroradiol. 2018;39(7):1201–1207. PMID: 29720536. doi:10.3174/ajnr.A5667 [doi] [pmid]
7. Akkus Z et al. Predicting patient survival and tumor grade in gliomas using deep learning on histopathological images. Neuro-Oncol. 2020;22(3):373–382. PMID: 31298274. doi:10.1093/neuonc/noz195 [doi] [pmid]
8. Perez F et al. Transfer learning for paediatric brain MRI segmentation using adult-trained convolutional neural networks. Front Oncol. 2024;14:1120478. doi:10.3389/fonc.2024.1120478 [doi]

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http://echo.ismrm.org/p/ISMRM2026/566-04-010