A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data
Ortega Martorell, Sandra (Liverpool John Moores University. Department of Mathematics and Statistics)
Ruiz, Héctor (Liverpool John Moores University. Department of Mathematics and Statistics)
Vellido, Alfredo (Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics)
Olier, Iván (University of Manchester. Institute of Population Health)
Romero, Enrique (Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics)
Julià Sapé, Ma. Margarita (Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina)
Martín, José D. (Universitat de València. Departament d'Enginyeria Electrònica)
Jarman, Ian H. (Liverpool John Moores University. Department of Mathematics and Statistics)
Arús i Caraltó, Carles (Universitat Autònoma de Barcelona. Departament de Bioquímica i de Biologia Molecular)
Lisboa, Paulo J. G. (Liverpool John Moores University. Department of Mathematics and Statistics)
Universitat Autònoma de Barcelona. Institut de Biotecnologia i de Biomedicina "Vicent Villar Palasí"

Fecha:	2013
Resumen:	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: 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: 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.
Derechos:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Lengua:	Anglès
Documento:	Article ; recerca ; Versió publicada
Materia:	Lipid signaling ; Algorithms ; Prototypes ; Data acquisition ; Magnetic resonance spectroscopy ; Cancer detection and diagnosis ; Glioblastoma multiforme ; Magnetic resonance imaging
Publicado en:	PloS one, Vol. 8 Issue 12 (December 2013) , p. e83773, ISSN 1932-6203

DOI: 10.1371/journal.pone.0083773
PMID: 24376744

14 p, 1.1 MB

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Documentos de investigación > Documentos de los grupos de investigación de la UAB > Centros y grupos de investigación (producción científica) > Ciencias de la salud y biociencias > Instituto de Biotecnología y de Biomedicina (IBB)
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