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

Grist, James T.; Withey, Stephanie; MacPherson, Lesley; Oates, Adam; Powell, Stephen; Novak, Jan; Abernethy, Laurence; Pizer, Barry; Grundy, Richard; Bailey, Simon; Mitra, Dipayan; Arvanitis, Theodoros N.; Auer, Dorothee P.; Avula, Shivaram; Peet, Andrew C

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


Authors

James T. Grist

Stephanie Withey

Lesley MacPherson

Adam Oates

Stephen Powell

Jan Novak

Laurence Abernethy

Barry Pizer

RICHARD GRUNDY richard.grundy@nottingham.ac.uk
Professor of Paediatric Neuro-Oncology

Simon Bailey

Dipayan Mitra

Theodoros N. Arvanitis

Dorothee P. Auer

Shivaram Avula

Andrew C Peet



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.

Citation

Grist, J. T., Withey, S., MacPherson, L., Oates, A., Powell, S., Novak, J., …Peet, A. C. (2020). Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning: A multi-site study. NeuroImage: Clinical, 25, Article 102172. https://doi.org/10.1016/j.nicl.2020.102172

Journal Article Type Article
Acceptance Date Jan 10, 2020
Online Publication Date Jan 23, 2020
Publication Date 2020
Deposit Date Mar 9, 2020
Publicly Available Date Mar 9, 2020
Journal NeuroImage: Clinical
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 25
Article Number 102172
DOI https://doi.org/10.1016/j.nicl.2020.102172
Keywords Cognitive Neuroscience; Radiology Nuclear Medicine and imaging; Neurology; Clinical Neurology
Public URL https://nottingham-repository.worktribe.com/output/4115577
Publisher URL https://www.sciencedirect.com/science/article/pii/S2213158220300115

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