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.
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.

Citation

(2013). A novel semi-supervised methodology for extracting tumor type-specific MRS sources in human brain data. PloS one, e83773 - e83773. https://doi.org/10.1371/journal.pone.0083773

• Acceptance Date: Nov 8, 2013
• Publication Date: Dec 23, 2013
• Journal: PloS one
• Print ISSN: 1932-6203
• Publisher: Public Library of Science
• Pages: e83773 - e83773
• DOI: https://doi.org/10.1371/journal.pone.0083773
• Keywords: Lipid signaling, Algorithms, Prototypes, Data acquisition, Magnetic resonance spectroscopy, Cancer detection and diagnosis, Glioblastoma multiforme, Magnetic resonance imaging
• Publisher URL: http://dx.plos.org/10.1371/journal.pone.0083773