On the relevance of automatically selected single-voxel MRS and multimodal MRI and MRSI features for brain tumour differentiation

Comput Biol Med. 2011 Feb;41(2):87-97. doi: 10.1016/j.compbiomed.2010.12.003. Epub 2011 Jan 13.

Abstract

In order to evaluate the relevance of magnetic resonance (MR) features selected by automatic feature selection techniques to build classifiers for differential diagnosis and tissue segmentation two data sets containing MR spectroscopy data from patients with brain tumours were investigated. The automatically selected features were evaluated using literature and clinical experience. It was observed that a significant part of the automatically selected features correspond to what is known from the literature and clinical experience. We conclude that automatic feature selection is a useful tool to obtain relevant and possibly interesting features, but evaluation of the obtained features remains necessary.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Analysis of Variance
  • Brain Chemistry
  • Brain Neoplasms / diagnosis*
  • Brain Neoplasms / metabolism
  • Brain Neoplasms / pathology
  • Computational Biology / methods*
  • Diagnosis, Differential
  • Discriminant Analysis
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Magnetic Resonance Imaging / methods*
  • Meningioma / diagnosis
  • Meningioma / metabolism
  • Meningioma / pathology
  • Neoplasm Metastasis / pathology
  • Statistics, Nonparametric