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
Grist, James T
Withey, Stephanie
MacPherson, Lesley
Oates, Adam
Powell, Stephen
Novak, Jan
Abernethy, Laurence
Pizer, Barry
Grundy, Richard
Bailey, Simon
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.
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Date
2020-01-23
Type
Article
Other
Other
Subject
Radiology, Paediatrics
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Citation
Grist JT, Withey S, MacPherson L, Oates A, Powell S, Novak J, Abernethy L, Pizer B, Grundy R, Bailey S, Mitra D, Arvanitis TN, Auer DP, Avula S, Peet AC. Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning: A multi-site study. Neuroimage Clin. 2020;25:102172. doi: 10.1016/j.nicl.2020.102172. Epub 2020 Jan 23
Journal / Source Title
NeuroImage. Clinical
DOI
10.1016/j.nicl.2020.102172
PMID
32032817
Publisher
Elsevier
Publisher’s URL
http://www.sciencedirect.com/science/journal/22131582