Toggle Main Menu Toggle Search

Open Access padlockePrints

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

Lookup NU author(s): Dr Dipayan Mitra, Professor Simon BaileyORCiD


Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


© 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.

Publication metadata

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

Year: 2018

Volume: 79

Issue: 4

Pages: 2359-2366

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.


DOI: 10.1002/mrm.26837


Altmetrics provided by Altmetric