Tumour grading from magnetic resonance spectroscopy: a comparison of feature extraction with variable selection

Magnetic resonance spectroscopy (MRS) provides a non‐invasive measurement of the biochemistry of living tissue. However, signal variation due to tissue heterogeneity causes considerable mixing between different disease categories, making accurate class assignments difficult. This paper compares a sy...

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Bibliographic Details
Published in:Statistics in medicine 2003-01, Vol.22 (1), p.147-164
Main Authors: Huang, Y., Lisboa, P. J. G., El-Deredy, W.
Format: Article
Language:English
Subjects:
Astrocytoma - pathology
Bayes Theorem
Biological and medical sciences
Brain Neoplasms - pathology
brain tumours
Data Interpretation, Statistical
Glioma - pathology
Humans
independent components
magnetic resonance
Medical sciences
model selection
Models, Statistical
Multivariate Analysis
Nuclear Magnetic Resonance, Biomolecular - methods
Retrospective Studies
spectroscopy
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Summary:Magnetic resonance spectroscopy (MRS) provides a non‐invasive measurement of the biochemistry of living tissue. However, signal variation due to tissue heterogeneity causes considerable mixing between different disease categories, making accurate class assignments difficult. This paper compares a systematic methodology for classifier design using multivariate bayesian variable selection (MBVS), with one based on feature extraction using independent component analysis (ICA). We illustrate the methodology and assess the classification performance using a data set comprising 41 magnetic resonance spectra acquired in vivo from two grades of brain tumour, namely low‐ and medium‐grade astrocytic tumours, labelled astrocytomas (AST), and high‐grade gliomas and glioblastomas labelled glioblastomas (GL). The aim of this study is threefold. First, to describe the application of the alternative methodologies to MRS, then to benchmark their classification performance, and finally to interpret the classification models in terms of biologically relevant signals derived from the spectra. The classification performance is assessed using the bootstrap method and by application to a test sample in a retrospective study. Copyright © 2003 John Wiley & Sons, Ltd.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.1321