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Abstract
Proton magnetic resonance spectroscopy (1H‐MRS) is increasingly used for clinical brain tumour diagnosis, but suffers from limited spectral quality. This retrospective and comparative study aims at improving paediatric brain tumour classification by performing noise suppression on clinical 1H‐MRS. Eighty‐three/forty‐two children with either an ependymoma (ages 4.6 ± $$ \pm $$ 5.3/9.3 ± $$ \pm $$ 5.4), a medulloblastoma (ages 6.9 ± $$ \pm $$ 3.5/6.5 ± $$ \pm $$ 4.4), or a pilocytic astrocytoma (8.0 ± $$ \pm $$ 3.6/6.3 ± $$ \pm $$ 5.0), recruited from four centres across England, were scanned with 1.5T/3T short‐echo‐time point‐resolved spectroscopy. The acquired raw 1H‐MRS was quantified by using Totally Automatic Robust Quantitation in NMR (TARQUIN), assessed by experienced spectroscopists, and processed with adaptive wavelet noise suppression (AWNS). Metabolite concentrations were extracted as features, selected based on multiclass receiver operating characteristics, and finally used for identifying brain tumour types with supervised machine learning. The minority class was oversampled through the synthetic minority oversampling technique for comparison purposes. Post‐noise‐suppression 1H‐MRS showed significantly elevated signal‐to‐noise ratios (P < .05, Wilcoxon signed‐rank test), stable full width at half‐maximum (P > .05, Wilcoxon signed‐rank test), and significantly higher classification accuracy (P < .05, Wilcoxon signed‐rank test). Specifically, the cross‐validated overall and balanced classification accuracies can be improved from 81% to 88% overall and 76% to 86% balanced for the 1.5T cohort, whilst for the 3T cohort they can be improved from 62% to 76% overall and 46% to 56%, by applying Naïve Bayes on the oversampled 1H‐MRS. The study shows that fitting‐based signal‐to‐noise ratios of clinical 1H‐MRS can be significantly improved by using AWNS with insignificantly altered line width, and the post‐noise‐suppression 1H‐MRS may have better diagnostic performance for paediatric brain tumours.
Original language | English |
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Article number | e5129 |
Number of pages | 40 |
Journal | NMR in biomedicine |
Early online date | 17 Mar 2024 |
DOIs | |
Publication status | E-pub ahead of print - 17 Mar 2024 |
Bibliographical note
Research Funding:Health Data Research UK
CHILDREN with CANCER UK
Action Medical Research and the Brain Tumour Charity
Birmingham Women's and Children's Hospital Charities
The Children's Cancer and Leukaemia Group Little Princess Trust
Cancer Research UK and NIHR Experimental Cancer Medicine Centre Paediatric Network
Children's Research Fund
NIHR Nottingham Biomedical Research Centre
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)
NIHR Research Professorship
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Keywords
- wavelet
- proton magnetic resonance spectroscopy
- machine learning
- noise suppression
- paediatric brain tumour
- metabolite concentration
ASJC Scopus subject areas
- Oncology
- Radiology Nuclear Medicine and imaging
- Cancer Research
- Spectroscopy
- Signal Processing
- Artificial Intelligence
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Dive into the research topics of 'Noise suppression of proton magnetic resonance spectroscopy improves paediatric brain tumour classification'. Together they form a unique fingerprint.Projects
- 2 Finished
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NIHR Professorship - Supporting Research into New Imaging Methods for Children
NIHR TRAINEES COORDINATING CENTRE
1/01/13 → 31/03/18
Project: Other Government Departments