Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Metabolite selection for machine learning in childhood brain tumour classification

Tools
- Tools
+ Tools

Zhao, Dadi, Grist, James T., Rose, Heather E. L., Davies, Nigel P., Wilson, Martin, MacPherson, Lesley, Abernethy, Laurence J., Avula, Shivaram, Pizer, Barry, Gutierrez, Daniel R., Jaspan, Tim, Morgan, Paul S., Mitra, Dipayan, Bailey, Simon, Sawlani, Vijay, Arvanitis, Theodoros N., Sun, Yu and Peet, Andrew C. (2022) Metabolite selection for machine learning in childhood brain tumour classification. NMR in Biomedicine, 35 (6). e4673. doi:10.1002/nbm.4673

[img]
Preview
PDF
WRAP-metabolite-selection-machine-learning-childhood-brain-tumour-classification-2022.pdf - Published Version - Requires a PDF viewer.
Available under License Creative Commons Attribution 4.0.

Download (3885Kb) | Preview
Official URL: https://doi.org/10.1002/nbm.4673

Request Changes to record.

Abstract

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, AUC = 0 . 81 ± 0 . 01 ), total lipids and macromolecules at 0.9 ppm (P < 0.05, AUC = 0 . 78 ± 0 . 01 ) and total creatine (P < 0.05, AUC = 0 . 77 ± 0 . 01 ) for the 1.5 T cohort, and glycine (P < 0.05, AUC = 0 . 79 ± 0 . 01 ), total N‐acetylaspartate (P < 0.05, AUC = 0 . 79 ± 0 . 01 ) and total choline (P < 0.05, AUC = 0 . 75 ± 0 . 01 ) 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.

Item Type: Journal Article
Subjects: Q Science > Q Science (General)
Q Science > QD Chemistry
Q Science > QP Physiology
R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Divisions: Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group)
SWORD Depositor: Library Publications Router
Library of Congress Subject Headings (LCSH): Brain -- Tumors -- Diagnosis, Intracranial tumors in children, Machine learning, Nuclear magnetic resonance spectroscopy -- Data processing, Metabolites
Journal or Publication Title: NMR in Biomedicine
Publisher: John Wiley & Sons Ltd.
ISSN: 0952-3480
Official Date: June 2022
Dates:
DateEvent
June 2022Published
27 January 2022Available
2 December 2021Accepted
Volume: 35
Number: 6
Article Number: e4673
DOI: 10.1002/nbm.4673
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
Doctoral ScholarshipHelp Harry Help Others Cancer Charityhttps://hhho.org.uk/
NIHR-RP-R2-12-019[NIHR] National Institute for Health Researchhttp://dx.doi.org/10.13039/501100000272
C7809/A10342Cancer Research UKhttp://dx.doi.org/10.13039/501100000289
C7809/A10342[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
C8232/A25261Cancer Research UKhttp://dx.doi.org/10.13039/501100000289
C8232/A25261[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
UNSPECIFIEDChildren's Research Fundhttps://www.childrensresearchfund.org/
UNSPECIFIEDBirmingham Women's and Children's Hospital CharitiesUNSPECIFIED
UNSPECIFIEDChildren's Cancer and Leukaemia Grouphttp://dx.doi.org/10.13039/100011692
UNSPECIFIEDHealth Data Research UKhttps://www.hdruk.ac.uk/

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us