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Classification of single‐voxel 1 H spectra of childhood cerebellar tumors using lcmodel and whole tissue representations
Author(s) -
Raschke Felix,
Davies Nigel P.,
Wilson Martin,
Peet Andrew C.,
Howe Franklyn A.
Publication year - 2013
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.24461
Subject(s) - medulloblastoma , pilocytic astrocytoma , astrocytoma , ependymoma , in vivo magnetic resonance spectroscopy , nuclear magnetic resonance , magnetic resonance imaging , medicine , glioma , pathology , radiology , physics , cancer research
In this study, mean tumor spectra are used as the basis functions in LCModel to create a direct classification tool for short echo time 1 H magnetic resonance spectroscopy of pediatric brain tumors. LCModel is a widely used analysis tool designed to fit a linear combination of individual metabolite spectra to in vivo spectra. Here, we have used LCModel to fit mean spectra and corresponding variability components of childhood cerebellar tumors, as calculated using principal component analysis, and assessed for classification accuracy. Classification was performed according to the highest estimated tumor proportion. This method was tested in a leave‐one‐out analysis discriminating between pediatric brain tumor spectra of medulloblastoma vs. pilocytic astrocytoma and medulloblastoma vs. pilocytic astrocytoma vs. ependymoma. Additionally, the effect of accepting different Cramér‐Rao Lower Bound cut‐off criteria on classification accuracy and estimated tissue proportions was investigated. The best classification results differentiating medulloblastoma vs. pilocytic astrocytoma and medulloblastoma vs. pilocytic astrocytoma vs. ependymoma were 100 and 87.7%, respectively. These results are comparable to a specialized pattern recognition analysis of this data set and give easy to interpret results in the form of estimated tissue proportions. The method requires minimal user input and is easily transferable across sites and to other magnetic resonance spectroscopy classification problems. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.