Gill, SK;
Rose, HEL;
Wilson, M;
Rodriguez Gutierrez, D;
Worthington, L;
Davies, NP;
MacPherson, L;
Hargrave, DR;
Saunders, DE;
Clark, CA;
et al.
Gill, SK; Rose, HEL; Wilson, M; Rodriguez Gutierrez, D; Worthington, L; Davies, NP; MacPherson, L; Hargrave, DR; Saunders, DE; Clark, CA; Payne, GS; Leach, MO; Howe, FA; Auer, DP; Jaspan, T; Morgan, PS; Grundy, RG; Avula, S; Pizer, B; Arvanitis, TN; Peet, AC
(2024)
Characterisation of paediatric brain tumours by their MRS metabolite profiles.
NMR Biomed, 37 (5).
e5101.
ISSN 1099-1492
https://doi.org/10.1002/nbm.5101
SGUL Authors: Howe, Franklyn Arron
Abstract
1 H-magnetic resonance spectroscopy (MRS) has the potential to improve the noninvasive diagnostic accuracy for paediatric brain tumours. However, studies analysing large, comprehensive, multicentre datasets are lacking, hindering translation to widespread clinical practice. Single-voxel MRS (point-resolved single-voxel spectroscopy sequence, 1.5 T: echo time [TE] 23-37 ms/135-144 ms, repetition time [TR] 1500 ms; 3 T: TE 37-41 ms/135-144 ms, TR 2000 ms) was performed from 2003 to 2012 during routine magnetic resonance imaging for a suspected brain tumour on 340 children from five hospitals with 464 spectra being available for analysis and 281 meeting quality control. Mean spectra were generated for 13 tumour types. Mann-Whitney U-tests and Kruskal-Wallis tests were used to compare mean metabolite concentrations. Receiver operator characteristic curves were used to determine the potential for individual metabolites to discriminate between specific tumour types. Principal component analysis followed by linear discriminant analysis was used to construct a classifier to discriminate the three main central nervous system tumour types in paediatrics. Mean concentrations of metabolites were shown to differ significantly between tumour types. Large variability existed across each tumour type, but individual metabolites were able to aid discrimination between some tumour types of importance. Complete metabolite profiles were found to be strongly characteristic of tumour type and, when combined with the machine learning methods, demonstrated a diagnostic accuracy of 93% for distinguishing between the three main tumour groups (medulloblastoma, pilocytic astrocytoma and ependymoma). The accuracy of this approach was similar even when data of marginal quality were included, greatly reducing the proportion of MRS excluded for poor quality. Children's brain tumours are strongly characterised by MRS metabolite profiles readily acquired during routine clinical practice, and this information can be used to support noninvasive diagnosis. This study provides both key evidence and an important resource for the future use of MRS in the diagnosis of children's brain tumours.
Item Type: |
Article
|
Additional Information: |
© 2024 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.
This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: |
1H-magnetic resonance spectroscopy, brain tumours, classification, metabolites, paediatric, 0304 Medicinal and Biomolecular Chemistry, 0903 Biomedical Engineering, 1103 Clinical Sciences, Nuclear Medicine & Medical Imaging |
SGUL Research Institute / Research Centre: |
Academic Structure > Molecular and Clinical Sciences Research Institute (MCS) |
Journal or Publication Title: |
NMR Biomed |
ISSN: |
1099-1492 |
Language: |
eng |
Dates: |
Date | Event |
---|
2 April 2024 | Published | 1 February 2024 | Published Online | 4 December 2023 | Accepted |
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Publisher License: |
Creative Commons: Attribution 4.0 |
Projects: |
|
PubMed ID: |
38303627 |
![](http://static.pubmed.gov/portal/portal3rc.fcgi/2745237/img/27842) |
Go to PubMed abstract |
URI: |
https://openaccess.sgul.ac.uk/id/eprint/116229 |
Publisher's version: |
https://doi.org/10.1002/nbm.5101 |
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