Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis
Braz. j. med. biol. res
;
44(2): 149-164, Feb. 2011. ilus, tab
Artigo
em Inglês
| LILACS
| ID: lil-573658
ABSTRACT
High resolution proton nuclear magnetic resonance spectroscopy (¹H MRS) can be used to detect biochemical changes in vitro caused by distinct pathologies. It can reveal distinct metabolic profiles of brain tumors although the accurate analysis and classification of different spectra remains a challenge. In this study, the pattern recognition method partial least squares discriminant analysis (PLS-DA) was used to classify 11.7 T ¹H MRS spectra of brain tissue extracts from patients with brain tumors into four classes (high-grade neuroglial, low-grade neuroglial, non-neuroglial, and metastasis) and a group of control brain tissue. PLS-DA revealed 9 metabolites as the most important in group differentiation γ-aminobutyric acid, acetoacetate, alanine, creatine, glutamate/glutamine, glycine, myo-inositol, N-acetylaspartate, and choline compounds. Leave-one-out cross-validation showed that PLS-DA was efficient in group characterization. The metabolic patterns detected can be explained on the basis of previous multimodal studies of tumor metabolism and are consistent with neoplastic cell abnormalities possibly related to high turnover, resistance to apoptosis, osmotic stress and tumor tendency to use alternative energetic pathways such as glycolysis and ketogenesis.
Texto completo:
DisponíveL
Índice:
LILACS (Américas)
Assunto principal:
Neoplasias Encefálicas
/
Espectroscopia de Ressonância Magnética
Tipo de estudo:
Estudo observacional
/
Estudo prognóstico
Limite:
Adulto
/
Idoso
/
Humanos
Idioma:
Inglês
Revista:
Braz. j. med. biol. res
Assunto da revista:
Biologia
/
Medicina
Ano de publicação:
2011
Tipo de documento:
Artigo
País de afiliação:
Brasil
Instituição/País de afiliação:
Universidade Estadual de Campinas/BR
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