Abstract
Purpose3T 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.MethodsA 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.ResultsSupport 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.ConclusionThis 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.
Highlights
Brain tumors are a significant cause of death and long-term disability in children, with a range of treatment options and outcomes depending on the tumor type, location, and age of the patient
The enrolled cohort consisted of patients with three different tumor types from all regions of the brain, including medulloblastoma (MB) (n 1⁄4 18), pilocytic astrocytoma (PA) (n 1⁄4 26), and ependymoma (EP) (n 1⁄4 8)
Approval was obtained from the research ethics committee and informed consent given by parents/guardians
Summary
Brain tumors are a significant cause of death and long-term disability in children, with a range of treatment options and outcomes depending on the tumor type, location, and age of the patient. Histopathology following biopsy or surgical resection is the current gold standard for diagnosis of brain tumor type and grade [1]. A pre-operative diagnosis can influence the extent of surgical resection attempted, allow the timely planning of adjuvant treatment and aid discussions with the family. Conventional magnetic resonance imaging (MRI) is commonly used to propose a diagnosis before surgery but is of limited accuracy. A previous study reviewed the radiological reports from a cohort of children with medulloblastoma, pilocytic astrocytoma, or ependymoma and showed an accuracy of diagnosis of 66% [2]
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