Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Journal of Medical and Biological Research |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2011000200009 |
Resumo: | 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. |
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Brazilian Journal of Medical and Biological Research |
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Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysisBrainTumorMagnetic resonance spectroscopySpectroscopyMetabolismHigh 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.Associação Brasileira de Divulgação Científica2011-02-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2011000200009Brazilian Journal of Medical and Biological Research v.44 n.2 2011reponame:Brazilian Journal of Medical and Biological Researchinstname:Associação Brasileira de Divulgação Científica (ABDC)instacron:ABDC10.1590/S0100-879X2010007500146info:eu-repo/semantics/openAccessFaria,A.V.Macedo Jr.,F.C.Marsaioli,A.J.Ferreira,M.M.C.Cendes,F.eng2016-09-26T00:00:00Zoai:scielo:S0100-879X2011000200009Revistahttps://www.bjournal.org/https://old.scielo.br/oai/scielo-oai.phpbjournal@terra.com.br||bjournal@terra.com.br1414-431X0100-879Xopendoar:2016-09-26T00:00Brazilian Journal of Medical and Biological Research - Associação Brasileira de Divulgação Científica (ABDC)false |
dc.title.none.fl_str_mv |
Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis |
title |
Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis |
spellingShingle |
Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis Faria,A.V. Brain Tumor Magnetic resonance spectroscopy Spectroscopy Metabolism |
title_short |
Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis |
title_full |
Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis |
title_fullStr |
Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis |
title_full_unstemmed |
Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis |
title_sort |
Classification of brain tumor extracts by high resolution ¹H MRS using partial least squares discriminant analysis |
author |
Faria,A.V. |
author_facet |
Faria,A.V. Macedo Jr.,F.C. Marsaioli,A.J. Ferreira,M.M.C. Cendes,F. |
author_role |
author |
author2 |
Macedo Jr.,F.C. Marsaioli,A.J. Ferreira,M.M.C. Cendes,F. |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Faria,A.V. Macedo Jr.,F.C. Marsaioli,A.J. Ferreira,M.M.C. Cendes,F. |
dc.subject.por.fl_str_mv |
Brain Tumor Magnetic resonance spectroscopy Spectroscopy Metabolism |
topic |
Brain Tumor Magnetic resonance spectroscopy Spectroscopy Metabolism |
description |
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. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-02-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2011000200009 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2011000200009 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/S0100-879X2010007500146 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Associação Brasileira de Divulgação Científica |
publisher.none.fl_str_mv |
Associação Brasileira de Divulgação Científica |
dc.source.none.fl_str_mv |
Brazilian Journal of Medical and Biological Research v.44 n.2 2011 reponame:Brazilian Journal of Medical and Biological Research instname:Associação Brasileira de Divulgação Científica (ABDC) instacron:ABDC |
instname_str |
Associação Brasileira de Divulgação Científica (ABDC) |
instacron_str |
ABDC |
institution |
ABDC |
reponame_str |
Brazilian Journal of Medical and Biological Research |
collection |
Brazilian Journal of Medical and Biological Research |
repository.name.fl_str_mv |
Brazilian Journal of Medical and Biological Research - Associação Brasileira de Divulgação Científica (ABDC) |
repository.mail.fl_str_mv |
bjournal@terra.com.br||bjournal@terra.com.br |
_version_ |
1754302939394998272 |