title: A novel semi-supervised methodology for extracting tumor type-specific MRS sources in human brain data.
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.
  
  
  RESULTS
  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.
authors.affiliation: Department of Mathematics and Statistics, Liverpool John Moores University, Liverpool, United Kingdom.
authors.forename: Sandra
authors.initials: S
authors.lastname: Ortega-Martorell
authors.affiliation: Department of Mathematics and Statistics, Liverpool John Moores University, Liverpool, United Kingdom.
authors.forename: Héctor
authors.initials: H
authors.lastname: Ruiz
authors.affiliation: Department of Computer Languages and Systems, Universitat Politècnica de Catalunya - BarcelonaTech, Barcelona, Spain.
authors.forename: Alfredo
authors.initials: A
authors.lastname: Vellido
authors.affiliation: Institute of Population Health, The University of Manchester, Manchester, United Kingdom.
authors.forename: Iván
authors.initials: I
authors.lastname: Olier
authors.affiliation: Department of Computer Languages and Systems, Universitat Politècnica de Catalunya - BarcelonaTech, Barcelona, Spain.
authors.forename: Enrique
authors.initials: E
authors.lastname: Romero
authors.affiliation: Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain ; Departament de Bioquímica i Biología Molecular, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain ; Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain.
authors.forename: Margarida
authors.initials: M
authors.lastname: Julià-Sapé
authors.affiliation: Departamento de Ingeniería Electrónica, Universidad de Valencia, Burjassot, Spain.
authors.forename: José D
authors.initials: JD
authors.lastname: Martín
authors.affiliation: Department of Mathematics and Statistics, Liverpool John Moores University, Liverpool, United Kingdom.
authors.forename: Ian H
authors.initials: IH
authors.lastname: Jarman
authors.affiliation: Departament de Bioquímica i Biología Molecular, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain ; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Cerdanyola del Vallès, Spain ; Institut de Biotecnologia i de Biomedicina, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain.
authors.forename: Carles
authors.initials: C
authors.lastname: Arús
authors.affiliation: Department of Mathematics and Statistics, Liverpool John Moores University, Liverpool, United Kingdom.
authors.forename: Paulo J G
authors.initials: PJ
authors.lastname: Lisboa
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology; D001932:Brain Neoplasms / Q000175:diagnosis* / Q000473:pathology; D006801:Humans; D009682:Magnetic Resonance Spectroscopy; D013223:Statistics as Topic / Q000379:methods*