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

@article{citeulike:1104631, abstract = {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.}, address = {School of Computing and Mathematical Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK.}, author = {Huang, Y. and Lisboa, P. J. and El-Deredy, W.}, citeulike-article-id = {1104631}, citeulike-linkout-0 = {http://dx.doi.org/10.1002/sim.1321}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/12486756}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=12486756}, day = {15}, doi = {10.1002/sim.1321}, issn = {0277-6715}, journal = {Stat Med}, keywords = {extraction, feature, mrs}, month = {January}, number = {1}, pages = {147--164}, posted-at = {2007-02-13 08:24:28}, priority = {5}, title = {Tumour grading from magnetic resonance spectroscopy: a comparison of feature extraction with variable selection.}, url = {http://dx.doi.org/10.1002/sim.1321}, volume = {22}, year = {2003} }

TY - JOUR ID - citeulike:1104631 L3 - citeulike-article-id:1104631 N2 - 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. IS - 1 JF - Stat Med SN - 0277-6715 AD - School of Computing and Mathematical Sciences, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK. EP - 164 TI - Tumour grading from magnetic resonance spectroscopy: a comparison of feature extraction with variable selection. VL - 22 SP - 147 KW - extraction KW - feature KW - mrs AU - Huang, Y AU - Lisboa, PJ AU - El-Deredy, W PY - 2003/01/15/ UR - http://dx.doi.org/10.1002/sim.1321 ER -