Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning: A multi-site study.

Grist, James T and Withey, Stephanie and MacPherson, Lesley and Oates, Adam and Powell, Stephen and Novak, Jan and Abernethy, Laurence and Pizer, Barry and Grundy, Richard and Bailey, Simon and Mitra, Dipayan and Arvanitis, Theodoros N and Auer, Dorothee P and Avula, Shivaram and Peet, Andrew C (2020) Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning: A multi-site study. NeuroImage. Clinical, 25. p. 102172. ISSN 2213-1582. This article is available to all UHB staff and students via ASK Discovery tool http://tinyurl.com/z795c8c by using their UHB Athens login IDs

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Abstract

The imaging and subsequent accurate diagnosis of paediatric brain tumours presents a radiological challenge, with magnetic resonance imaging playing a key role in providing tumour specific imaging information. Diffusion weighted and perfusion imaging are commonly used to aid the non-invasive diagnosis of children's brain tumours, but are usually evaluated by expert qualitative review. Quantitative studies are mainly single centre and single modality. The aim of this work was to combine multi-centre diffusion and perfusion imaging, with machine learning, to develop machine learning based classifiers to discriminate between three common paediatric tumour types. The results show that diffusion and perfusion weighted imaging of both the tumour and whole brain provide significant features which differ between tumour types, and that combining these features gives the optimal machine learning classifier with >80% predictive precision. This work represents a step forward to aid in the non-invasive diagnosis of paediatric brain tumours, using advanced clinical imaging.

Item Type: Article
Additional Information: This article is available to all UHB staff and students via ASK Discovery tool http://tinyurl.com/z795c8c by using their UHB Athens login IDs
Subjects: QZ Pathology. Oncology
WL Nervous system. Neurology
WN Medical imaging. Radiology
WS Paediatrics. Child health
Divisions: Emergency Services > Neurology
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Depositing User: Jamie Edgar
Date Deposited: 14 Feb 2020 13:21
Last Modified: 14 Feb 2020 13:21
URI: http://www.repository.uhblibrary.co.uk/id/eprint/2846

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