Metabolite selection for machine learning in childhood brain tumour classification.
Author
Zhao, DadiGrist, 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
Peet, Andrew C
Publication date
2022-01-27
Metadata
Show full item recordAbstract
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 1 H-MRS through support vector machine and 75% for 3 T 1 H-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.Citation
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. Metabolite selection for machine learning in childhood brain tumour classification. NMR Biomed. 2022 Jun;35(6):e4673. doi: 10.1002/nbm.4673. Epub 2022 Jan 27Type
ArticleAdditional Links
http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-1492DOI
10.1002/nbm.4673PMID
35088473Journal
NMR in BiomedicinePublisher
Wileyae974a485f413a2113503eed53cd6c53
10.1002/nbm.4673