| Autor: |
Raschke, F., Fuster-Garcia, E., Opstad, K. S., Howe, F. A. |
| Zdroj: |
NMR in Biomedicine; Feb2012, Vol. 25 Issue 2, p322-331, 10p |
| Abstrakt: |
This study presents a novel method for the direct classification of 1H 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 © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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