A novel semi-supervised methodology for extracting tumor type-specific MRS sources in human brain data.
<h4>Background</h4>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 i...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2013-01-01
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| Series: | PLoS ONE |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24376744/pdf/?tool=EBI |
| _version_ | 1818338017880834048 |
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| author | Sandra Ortega-Martorell Héctor Ruiz Alfredo Vellido Iván Olier Enrique Romero Margarida Julià-Sapé José D Martín Ian H Jarman Carles Arús Paulo J G Lisboa |
| author_facet | Sandra Ortega-Martorell Héctor Ruiz Alfredo Vellido Iván Olier Enrique Romero Margarida Julià-Sapé José D Martín Ian H Jarman Carles Arús Paulo J G Lisboa |
| author_sort | Sandra Ortega-Martorell |
| collection | DOAJ |
| description | <h4>Background</h4>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.<h4>Methodology/principal findings</h4>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.<h4>Conclusions/significance</h4>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. |
| first_indexed | 2024-12-13T15:04:26Z |
| format | Article |
| id | doaj.art-cad2e552d41c4d049216a0d24458c15f |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T15:04:26Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-cad2e552d41c4d049216a0d24458c15f2022-12-21T23:41:03ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-01812e8377310.1371/journal.pone.0083773A novel semi-supervised methodology for extracting tumor type-specific MRS sources in human brain data.Sandra Ortega-MartorellHéctor RuizAlfredo VellidoIván OlierEnrique RomeroMargarida Julià-SapéJosé D MartínIan H JarmanCarles ArúsPaulo J G Lisboa<h4>Background</h4>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.<h4>Methodology/principal findings</h4>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.<h4>Conclusions/significance</h4>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.https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24376744/pdf/?tool=EBI |
| spellingShingle | Sandra Ortega-Martorell Héctor Ruiz Alfredo Vellido Iván Olier Enrique Romero Margarida Julià-Sapé José D Martín Ian H Jarman Carles Arús Paulo J G Lisboa A novel semi-supervised methodology for extracting tumor type-specific MRS sources in human brain data. PLoS ONE |
| 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 |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24376744/pdf/?tool=EBI |
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