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...
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2013
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oai:doaj.org-article:cad2e552d41c4d049216a0d24458c15f2021-11-18T08:40:34ZA novel semi-supervised methodology for extracting tumor type-specific MRS sources in human brain data.1932-620310.1371/journal.pone.0083773https://doaj.org/article/cad2e552d41c4d049216a0d24458c15f2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24376744/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<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.Sandra Ortega-MartorellHéctor RuizAlfredo VellidoIván OlierEnrique RomeroMargarida Julià-SapéJosé D MartínIan H JarmanCarles ArúsPaulo J G LisboaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 12, p e83773 (2013) |
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Medicine R Science Q 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. |
| 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. |
| format |
article |
| 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 |
| title |
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_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_sort |
novel semi-supervised methodology for extracting tumor type-specific mrs sources in human brain data. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2013 |
| url |
https://doaj.org/article/cad2e552d41c4d049216a0d24458c15f |
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