A metabolomic data fusion approach to support gliomas grading.
Brain tumors
Classification
Double cross-validation
Gliomas
HR-MAS NMR
Metabolomics
Multivariate Curve Resolution
SIMCA
Journal
NMR in biomedicine
ISSN: 1099-1492
Titre abrégé: NMR Biomed
Pays: England
ID NLM: 8915233
Informations de publication
Date de publication:
03 2020
03 2020
Historique:
received:
16
07
2019
revised:
12
11
2019
accepted:
12
11
2019
pubmed:
12
12
2019
medline:
8
1
2021
entrez:
12
12
2019
Statut:
ppublish
Résumé
Magnetic resonance imaging (MRI) is the current gold standard for the diagnosis of brain tumors. However, despite the development of MRI techniques, the differential diagnosis of central nervous system (CNS) primary pathologies, such as lymphoma and glioblastoma or tumor-like brain lesions and glioma, is often challenging. MRI can be supported by in vivo magnetic resonance spectroscopy (MRS) to enhance its diagnostic power and multiproject-multicenter evaluations of classification of brain tumors have shown that an accuracy around 90% can be achieved for most of the pairwise discrimination problems. However, the survival rate for patients affected by gliomas is still low. The High-Resolution Magic-Angle-Spinning Nuclear Magnetic Resonance (HR-MAS NMR) metabolomics studies may be helpful for the discrimination of gliomas grades and the development of new strategies for clinical intervention. Here, we propose to use T
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e4234Informations de copyright
© 2019 John Wiley & Sons, Ltd.
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