Ortega-Martorell, S and Ruiz, H and Vellido, A and Olier, I and Romero, E and Julià-Sapé, M and Martín, JD and Jarman, IH and Arús, C and Lisboa, PJG (2013) A novel semi-supervised methodology for extracting tumor type-specific MRS sources in human brain data. PLoS One, 8 (12). e83773 - e83773. ISSN 1932-6203

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: Lipid signaling, Algorithms, Prototypes, Data acquisition, Magnetic resonance spectroscopy, Cancer detection and diagnosis, Glioblastoma multiforme, Magnetic resonance imaging
Subjects: R Medicine > R Medicine (General)
Divisions: Faculty of Medicine and Health Sciences > Institute for Science and Technology in Medicine
Depositing User: Symplectic
Date Deposited: 10 Nov 2015 16:06
Last Modified: 30 Nov 2016 14:39
URI: http://eprints.keele.ac.uk/id/eprint/1121

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