Toggle Main Menu Toggle Search

Open Access padlockePrints

Metabolite selection for machine learning in childhood brain tumour classification

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



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2022 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd. MRS can provide high accuracy in the diagnosis of childhood brain tumours when combined with machine learning. A feature selection method such as principal component analysis is commonly used to reduce the dimensionality of metabolite profiles prior to classification. However, an alternative approach of identifying the optimal set of metabolites has not been fully evaluated, possibly due to the challenges of defining this for a multi-class problem. This study aims to investigate metabolite selection from in vivo MRS for childhood brain tumour classification. Multi-site 1.5 T and 3 T cohorts of patients with a brain tumour and histological diagnosis of ependymoma, medulloblastoma and pilocytic astrocytoma were retrospectively evaluated. Dimensionality reduction was undertaken by selecting metabolite concentrations through multi-class receiver operating characteristics and compared with principal component analysis. Classification accuracy was determined through leave-one-out and k-fold cross-validation. Metabolites identified as crucial in tumour classification include myo-inositol (P < 0.05, (Formula presented.)), total lipids and macromolecules at 0.9 ppm (P < 0.05, (Formula presented.)) and total creatine (P < 0.05, (Formula presented.)) for the 1.5 T cohort, and glycine (P < 0.05, (Formula presented.)), total N-acetylaspartate (P < 0.05, (Formula presented.)) and total choline (P < 0.05, (Formula presented.)) for the 3 T cohort. Compared with the principal components, the selected metabolites were able to provide significantly improved discrimination between the tumours through most classifiers (P < 0.05). The highest balanced classification accuracy determined through leave-one-out cross-validation was 85% for 1.5 T 1H-MRS through support vector machine and 75% for 3 T 1H-MRS through linear discriminant analysis after oversampling the minority. The study suggests that a group of crucial metabolites helps to achieve better discrimination between childhood brain tumours.

Publication metadata

Author(s): Zhao D, Grist JT, Rose HEL, Davies NP, Wilson M, MacPherson L, Abernethy LJ, Avula S, Pizer B, Gutierrez DR, Jaspan T, Morgan PS, Mitra D, Bailey S, Sawlani V, Arvanitis TN, Sun Y, Peet AC

Publication type: Article

Publication status: Published

Journal: NMR in Biomedicine

Year: 2022

Pages: Epub ahead of print

Online publication date: 27/01/2022

Acceptance date: 02/12/2021

Date deposited: 07/03/2022

ISSN (print): 0952-3480

ISSN (electronic): 1099-1492

Publisher: John Wiley and Sons Ltd


DOI: 10.1002/nbm.4673


Altmetrics provided by Altmetric


Funder referenceFunder name