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

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.

Publication DOI: https://doi.org/10.1016/j.nicl.2020.102172
Divisions: College of Health & Life Sciences > School of Psychology
College of Health & Life Sciences
Additional Information: © 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/). Funding: We would like to acknowledge funding from the Cancer Research UK and EPSRC Cancer Imaging Programme at the Children's Cancer and Leukaemia Group (CCLG) in association with the MRC and Department of Health (England) (C7809/A10342), the Cancer Research UK and NIHR Experimental Cancer Medicine Centre Paediatric Network (C8232/A25261), the Medical Research Council – Health Data Research UK Substantive Site and Help Harry Help Others charity. Professor Peet is funded through an NIHR Research Professorship, NIHR-RP-R2-12-019. Stephen Powell gratefully acknowledges financial support from EPSRC through a studentship from the Physical Sciences for Health Centre for Doctoral Training (EP/L016346/1). Professor Theodoros N Arvanitis is partially supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome Trust. We would also like to acknowledge the MR radiographers at Birmingham Children's Hospital, Alder Hey Children's Hospital, the Royal Victoria Infirmary in Newcastle and Nottingham Children's Hospital for scanning the patients in this study. We would also like to thank Selene Rowe at Nottingham University Hospitals NHS Trust for help with gaining MRI protocol information. Dr James Grist is funded by the Little Princess Trust (CCLGA 2017 15).
Uncontrolled Keywords: Diffusion,Machine learning,Perfusion,Radiology Nuclear Medicine and imaging,Neurology,Clinical Neurology,Cognitive Neuroscience
Publication ISSN: 2213-1582
Last Modified: 13 May 2024 17:49
Date Deposited: 27 Jan 2020 14:06
Full Text Link:
Related URLs: https://linking ... 213158220300115 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2020-02-15
Published Online Date: 2020-01-23
Accepted Date: 2020-01-10
Authors: Grist, James T.
Withey, Stephanie
Macpherson, Lesley
Oates, Adam
Powell, Stephen
Novak, Jan (ORCID Profile 0000-0001-5173-3608)
Abernethy, Laurence
Pizer, Barry
Grundy, Richard
Bailey, Simon
Mitra, Dipayan
Arvanitis, Theodoros N.
Auer, Dorothee P.
Avula, Shivaram
Peet, Andrew C

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