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Abstract #4690

Utilising multi-parametric MRI to non-invasively predict tumour type in paediatric neuro-oncological disease: a multi-centre study.

James Timothy Grist1, Stephanie Timothy Withey2, Lesley MacPherson3, Adam Oates4, Stephen Timothy Powell2, Jan Novak5, Laurence Abernethy6, Barry Pizer7, Ricahrd Grundy8, Simon Bailey9, Dipayan Mitra9, Theodoros N Arvantis10, Dorothee P. Auer8, and Andrew C Peet2
1University of Birmingham, BIRMINGHAM, United Kingdom, 2University of Birmingham, Birmingham, United Kingdom, 3Birmingham Women's and CHildren's NHS foundation trust, Birmingham, United Kingdom, 4Birmingham Women's and Children's NHS foundation trust, Birmingham, United Kingdom, 5Aston University, Birmingham, United Kingdom, 6Alder Hey Children's NHS foundation trust, Liverpool, United Kingdom, 7Institute of Translation Medicine, University of Liverpool, Liverpool, United Kingdom, 8University of Nottingham, Nottingham, United Kingdom, 9Royal Victoria Infirmary, Newcastle, United Kingdom, 10University of Warwick, Warwick, United Kingdom

This study focuses on utilising supervised Machine Learning to combine both diffusion and perfusion weighted imaging to discriminate between the three most common pediatric brain tumour types: Pilocytic Astrocytoma, Ependymoma, and Medulloblastoma.

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