title: Classification of single-voxel 1H spectra of brain tumours using LCModel.
abstract: This study presents a novel method for the direct classification of (1)H 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.
authors.affiliation: Division of Clinical Sciences, St. George's University of London, London, UK. fraschke@sgul.ac.uk
authors.forename: F
authors.initials: F
authors.lastname: Raschke
authors.forename: E
authors.initials: E
authors.lastname: Fuster-Garcia
authors.forename: K S
authors.initials: KS
authors.lastname: Opstad
authors.forename: F A
authors.initials: FA
authors.lastname: Howe
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000473:pathology; D001932:Brain Neoplasms / Q000473:pathology*; D020000:Decision Support Systems, Clinical; D005910:Glioma / Q000473:pathology; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000145:classification* / Q000379:methods*; D060787:Neoplasm Grading; D009928:Organ Specificity; D011522:Protons
chemical_list: D011522:Protons