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Application of pattern recognition techniques for classification of pediatric brain tumors by in vivo 3T 1 H‐MR spectroscopy—A multi‐center study
Author(s) -
Zarinabad Niloufar,
Abernethy Laurence J.,
Avula Shivaram,
Davies Nigel P.,
Rodriguez Gutierrez Daniel,
Jaspan Tim,
MacPherson Lesley,
Mitra Dipayan,
Rose Heather E.L.,
Wilson Martin,
Morgan Paul S.,
Bailey Simon,
Pizer Barry,
Arvanitis Theodoros N.,
Grundy Richard G.,
Auer Dorothee P.,
Peet Andrew
Publication year - 2018
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.26837
Subject(s) - in vivo magnetic resonance spectroscopy , artificial intelligence , random forest , support vector machine , computer science , magnetic resonance imaging , brain tumor , discriminative model , machine learning , medicine , pattern recognition (psychology) , radiology , pathology
Purpose 3T magnetic resonance scanners have boosted clinical application of 1 H‐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.