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A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data

BACKGROUND: The clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying...

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Autores principales: Ortega-Martorell, Sandra, Ruiz, Héctor, Vellido, Alfredo, Olier, Iván, Romero, Enrique, Julià-Sapé, Margarida, Martín, José D., Jarman, Ian H., Arús, Carles, Lisboa, Paulo J. G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3871596/
https://www.ncbi.nlm.nih.gov/pubmed/24376744
http://dx.doi.org/10.1371/journal.pone.0083773
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author Ortega-Martorell, Sandra
Ruiz, Héctor
Vellido, Alfredo
Olier, Iván
Romero, Enrique
Julià-Sapé, Margarida
Martín, José D.
Jarman, Ian H.
Arús, Carles
Lisboa, Paulo J. G.
author_facet Ortega-Martorell, Sandra
Ruiz, Héctor
Vellido, Alfredo
Olier, Iván
Romero, Enrique
Julià-Sapé, Margarida
Martín, José D.
Jarman, Ian H.
Arús, Carles
Lisboa, Paulo J. G.
author_sort Ortega-Martorell, Sandra
collection PubMed
description BACKGROUND: The clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyzes single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of tumor type classification from the spectroscopic signal. METHODOLOGY/PRINCIPAL FINDINGS: Non-negative matrix factorization techniques have recently shown their potential for the identification of meaningful sources from brain tissue spectroscopy data. In this study, we use a convex variant of these methods that is capable of handling negatively-valued data and generating sources that can be interpreted as tumor class prototypes. A novel approach to convex non-negative matrix factorization is proposed, in which prior knowledge about class information is utilized in model optimization. Class-specific information is integrated into this semi-supervised process by setting the metric of a latent variable space where the matrix factorization is carried out. The reported experimental study comprises 196 cases from different tumor types drawn from two international, multi-center databases. The results indicate that the proposed approach outperforms a purely unsupervised process by achieving near perfect correlation of the extracted sources with the mean spectra of the tumor types. It also improves tissue type classification. CONCLUSIONS/SIGNIFICANCE: We show that source extraction by unsupervised matrix factorization benefits from the integration of the available class information, so operating in a semi-supervised learning manner, for discriminative source identification and brain tumor labeling from single-voxel spectroscopy data. We are confident that the proposed methodology has wider applicability for biomedical signal processing.
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spelling pubmed-38715962013-12-27 A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data Ortega-Martorell, Sandra Ruiz, Héctor Vellido, Alfredo Olier, Iván Romero, Enrique Julià-Sapé, Margarida Martín, José D. Jarman, Ian H. Arús, Carles Lisboa, Paulo J. G. PLoS One Research Article BACKGROUND: The clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyzes single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of tumor type classification from the spectroscopic signal. METHODOLOGY/PRINCIPAL FINDINGS: Non-negative matrix factorization techniques have recently shown their potential for the identification of meaningful sources from brain tissue spectroscopy data. In this study, we use a convex variant of these methods that is capable of handling negatively-valued data and generating sources that can be interpreted as tumor class prototypes. A novel approach to convex non-negative matrix factorization is proposed, in which prior knowledge about class information is utilized in model optimization. Class-specific information is integrated into this semi-supervised process by setting the metric of a latent variable space where the matrix factorization is carried out. The reported experimental study comprises 196 cases from different tumor types drawn from two international, multi-center databases. The results indicate that the proposed approach outperforms a purely unsupervised process by achieving near perfect correlation of the extracted sources with the mean spectra of the tumor types. It also improves tissue type classification. CONCLUSIONS/SIGNIFICANCE: We show that source extraction by unsupervised matrix factorization benefits from the integration of the available class information, so operating in a semi-supervised learning manner, for discriminative source identification and brain tumor labeling from single-voxel spectroscopy data. We are confident that the proposed methodology has wider applicability for biomedical signal processing. Public Library of Science 2013-12-23 /pmc/articles/PMC3871596/ /pubmed/24376744 http://dx.doi.org/10.1371/journal.pone.0083773 Text en © 2013 Ortega-Martorell et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Ortega-Martorell, Sandra
Ruiz, Héctor
Vellido, Alfredo
Olier, Iván
Romero, Enrique
Julià-Sapé, Margarida
Martín, José D.
Jarman, Ian H.
Arús, Carles
Lisboa, Paulo J. G.
A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data
title A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data
title_full A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data
title_fullStr A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data
title_full_unstemmed A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data
title_short A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data
title_sort novel semi-supervised methodology for extracting tumor type-specific mrs sources in human brain data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3871596/
https://www.ncbi.nlm.nih.gov/pubmed/24376744
http://dx.doi.org/10.1371/journal.pone.0083773
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