NAGIOS: RODERIC FUNCIONANDO

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

Repositori DSpace/Manakin

IMPORTANT: Aquest repositori està en una versió antiga des del 3/12/2023. La nova instal.lació está en https://roderic.uv.es/

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

dc.contributor.author	Ortega-Martorell, Sandra	es_ES
dc.contributor.author	Ruiz, Héctor	es_ES
dc.contributor.author	Vellido, Alfredo	es_ES
dc.contributor.author	Olier, Iván	es_ES
dc.contributor.author	Romero, Enrique	es_ES
dc.contributor.author	Julià-Sapé, Margarida	es_ES
dc.contributor.author	Martín, José D.	es_ES
dc.contributor.author	Jarman, Ian H.	es_ES
dc.contributor.author	Arús, Carles	es_ES
dc.contributor.author	Lisboa, Paulo J. G.	es_ES
dc.date.accessioned	2015-06-19T07:47:43Z
dc.date.available	2015-06-19T07:47:43Z
dc.date.issued	2013	es_ES
dc.identifier.citation	PLoS ONE Vol. 8 Issue 12:	es_ES
dc.identifier.uri	http://hdl.handle.net/10550/44492
dc.description.abstract	BackgroundThe 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 FindingsNon-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/SignificanceWe 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.	es_ES
dc.title	A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data	es_ES
dc.type	journal article	es_ES
dc.identifier.doi	10.1371/journal.pone.0083773	es_ES
dc.identifier.idgrec	092933	es_ES