Classification of single-voxel 1H spectra of brain tumours using LCModel

Autor: Raschke, Felix, Fuster García, Elíes|||0000-0002-0716-8960, Opstad, K. S., Howe, Franklyn
Rok vydání: 2010
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Zdroj: RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
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ISSN: 1099-1492
Popis: [EN] This study presents a novel method for the direct classification of H-1 single-voxel MR brain tumour spectra using the widespread analysis tool LCModel. LCModel is designed to estimate individual metabolite proportions by fitting a linear combination of in vitro metabolite spectra to an in vivo MR spectrum. In this study, it is used to fit representations of complete tumour spectra and to perform a classification according to the highest estimated tissue proportion. Each tumour type is represented by two spectra, a mean component and a variability term, as calculated using a principal component analysis of a training dataset. In the same manner, a mean component and a variability term for normal white matter are also added into the analysis to allow a mixed tissue approach. An unbiased evaluation of the method is carried out through the automatic selection of training and test sets using the Kennard and Stone algorithm, and a comparison of LCModel classification results with those of the INTERPRET Decision Support System (IDSS) which incorporates an advanced pattern recognition method. In a test set of 46 spectra comprising glioblastoma multiforme, low-grade gliomas and meningiomas, LCModel gives a classification accuracy of 90% compared with an accuracy of 95% by IDSS. Copyright (C) 2011 John Wiley & Sons, Ltd.
FR was supported by grant C7809/A10342 as part of the Cancer Research-UK and Engineering and Physical Sciences Research Council Cancer Imaging Programme at the Children's Cancer and Leukaemia Group (CCLG), in association with the Medical Research Council and Department of Health (England). EF-G acknowledges funding by the Health Institute Carlos III through the RETICS Combiomed.
Databáze: OpenAIRE
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