Application of pattern recognition techniques for classification of pediatric brain tumors by in vivo 3T 1H‐MR spectroscopy—A multi‐center study

Research output: Contribution to journalArticlepeer-review

Authors

  • Laurence J. Abernethy
  • Shivaram Avula
  • Nigel Davies
  • Daniel Rodriguez Gutierrez
  • Tim Jaspan
  • Lesley Macpherson
  • Dipayan Mitra
  • Paul S. Morgan
  • Simon Bailey
  • Barry Pizer
  • Richard G. Grundy
  • Dorothee P. Auer

External organisations

  • Department of Radiology, Alder Hey Children's NHS Foundation Trust
  • The Children's Brain Tumour Research Centre, University of Nottingham
  • Birmingham Children's Hospital
  • Neuroradiology Department, Newcastle upon Tyne Hospitals
  • Paediatric Oncology Department, Great North Children's Hospital
  • Department of Paediatric Oncology, Alder Hey Children's NHS Foundation Trust
  • The Children's Brain Tumour Research Centre, University of Nottingham
  • Sir James Spence Institute of Child Health
  • Royal Victoria Infirmary

Abstract

Purpose: 3T magnetic resonance scanners have boosted clinical application of 1H‐MR spectroscopy (MRS) by offering an improved signal‐to‐noise ratio and increased spectral resolution, thereby identifying more metabolites and extending the range of metabolic information. Spectroscopic data from clinical 1.5T MR scanners has been shown to discriminate between pediatric brain tumors by applying machine learning techniques to further aid diagnosis. The purpose of this multi‐center study was to investigate the discriminative potential of metabolite profiles obtained from 3T scanners in classifying pediatric brain tumors.

Methods: A total of 41 pediatric patients with brain tumors (17 medulloblastomas, 20 pilocytic astrocytomas, and 4 ependymomas) were scanned across four different hospitals. Raw spectroscopy data were processed using TARQUIN. Borderline synthetic minority oversampling technique was used to correct for the data skewness. Different classifiers were trained using linear discriminative analysis, support vector machine, and random forest techniques.

Results: Support vector machine had the highest balanced accuracy for discriminating the three tumor types. The balanced accuracy achieved was higher than the balanced accuracy previously reported for similar multi‐center dataset from 1.5T magnets with echo time 20 to 32 ms alone.

Conclusion: This study showed that 3T MRS can detect key differences in metabolite profiles for the main types of childhood tumors. Magn Reson Med 79:2359–2366, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Details

Original languageEnglish
Pages (from-to)2359-2366
Number of pages8
JournalMagnetic Resonance in Medicine
Volume79
Issue number4
Early online date8 Aug 2017
Publication statusPublished - Apr 2018

Keywords

  • MR specroscopy , 3T , pediatric brain tumors , diagnosis classification