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
Article
in English
| 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.
Full text:
Available
Index:
LILACS (Americas)
Main subject:
Brain Neoplasms
/
Magnetic Resonance Spectroscopy
Type of study:
Observational study
/
Prognostic study
Limits:
Adult
/
Aged
/
Humans
Language:
English
Journal:
Braz. j. med. biol. res
Journal subject:
Biology
/
Medicine
Year:
2011
Type:
Article
Affiliation country:
Brazil
Institution/Affiliation country:
Universidade Estadual de Campinas/BR
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