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Lookup NU author(s): Dr Dipayan Mitra,
Professor Simon Bailey
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© 2017 International Society for Magnetic Resonance in Medicine 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 International Society for Magnetic Resonance in Medicine.
Author(s): Zarinabad N, Abernethy LJ, Avula S, Davies NP, Rodriguez Gutierrez D, Jaspan T, MacPherson L, Mitra D, Rose HEL, Wilson M, Morgan PS, Bailey S, Pizer B, Arvanitis TN, Grundy RG, Auer DP, Peet A
Publication type: Article
Publication status: Published
Journal: Magnetic Resonance in Medicine
Print publication date: 01/04/2018
Online publication date: 08/08/2017
Acceptance date: 23/06/2017
ISSN (print): 0740-3194
ISSN (electronic): 1522-2594
Publisher: John Wiley and Sons Inc.
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