Noise suppression of proton magnetic resonance spectroscopy improves paediatric brain tumour classification

Author:

Zhao Teddy12ORCID,Grist James T.1ORCID,Auer Dorothee P.3ORCID,Avula Shivaram4ORCID,Bailey Simon5ORCID,Davies Nigel P.6ORCID,Grundy Richard G.3ORCID,Khan Omar7ORCID,MacPherson Lesley8ORCID,Morgan Paul S.3910ORCID,Pizer Barry11,Rose Heather E. L.12ORCID,Sun Yu12ORCID,Wilson Martin12ORCID,Worthington Lara1213,Arvanitis Theodoros N.12714ORCID,Peet Andrew C.12ORCID

Affiliation:

1. Cancer and Genomic Sciences University of Birmingham Birmingham UK

2. Oncology Birmingham Children's Hospital Birmingham UK

3. Clinical Neuroscience University of Nottingham Nottingham UK

4. Radiology Alder Hey Children's NHS Foundation Trust Liverpool UK

5. Paediatric Oncology Great North Children's Hospital Newcastle upon Tyne UK

6. Imaging and Medical Physics University Hospitals Birmingham NHS Foundation Trust Birmingham UK

7. Digital Healthcare, WMG University of Warwick Coventry UK

8. Radiology Birmingham Children's Hospital Birmingham UK

9. Children's Brain Tumour Research Centre University of Nottingham Nottingham UK

10. Medical Physics Nottingham University Hospitals NHS Trust Nottingham UK

11. University of Liverpool Liverpool UK

12. Centre for Human Brain Health University of Birmingham Birmingham UK

13. RRPPS University Hospitals Birmingham NHS Foundation Trust Birmingham UK

14. Engineering University of Birmingham Birmingham UK

Abstract

AbstractProton 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 5.3/9.3 5.4), a medulloblastoma (ages 6.9 3.5/6.5 4.4), or a pilocytic astrocytoma (8.0 3.6/6.3 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.

Funder

Health Data Research UK

CHILDREN with CANCER UK

North of England Children’s Cancer Research Fund

NIHR Nottingham Biomedical Research Centre

Publisher

Wiley

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