Towards a method for automated classification of 1H MRS spectra from brain tumours

NMR Biomed. 1998 Jun-Aug;11(4-5):177-91. doi: 10.1002/(sici)1099-1492(199806/08)11:4/5<177::aid-nbm534>;2-u.


Recent studies have shown that MRS can substantially improve the non-invasive categorization of human brain tumours. However, in order for MRS to be used routinely by clinicians, it will be necessary to develop reliable automated classification methods that can be fully validated. This paper is in two parts: the first part reviews the progress that has been made towards this goal, together with the problems that are involved in the design of automated methods to process and classify the spectra. The second part describes the development of a simple prototype system for classifying 1H single voxel spectra, obtained at an echo time (TE) of 135 ms, of the four most common types of brain tumour (meningioma (MM), astrocytic (AST), oligodendroglioma (OD) and metastasis (ME)) and cysts. This system was developed in two stages: firstly, an initial database of spectra was used to develop a prototype classifier, based on a linear discriminant analysis (LDA) of selected data points. Secondly, this classifier was tested on an independent test set of 15 newly acquired spectra, and the system was refined on the basis of these results. The system correctly classified all the non-astrocytic tumours. However, the results for the the astrocytic group were poorer (between 55 and 100%, depending on the binary comparison). Approximately 50% of high grade astrocytoma (glioblastoma) spectra in our data base showed very little lipid signal, which may account for the poorer results for this class. Consequently, for the refined system, the astrocytomas were subdivided into two subgroups for comparison against other tumour classes: those with high lipid content and those without.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Brain Neoplasms / classification*
  • Brain Neoplasms / diagnosis*
  • Data Interpretation, Statistical
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Nuclear Magnetic Resonance, Biomolecular / methods*
  • Pattern Recognition, Automated