Tumour grading from magnetic resonance spectroscopy: a comparison of feature extraction with variable selection

Abstract

Magnetic resonance spectroscopy (MRS) provides a non-invasive measurement of the biochemistry of living tissue. However, signal variation due to tissue heterogeneity causes considerable mixing between different disease categories, making accurate class assignments difficult. This paper compares a systematic methodology for classifier design using multivariate bayesian variable selection (MBVS), with one based on feature extraction using independent component analysis (ICA). We illustrate the methodology and assess the classification performance using a data set comprising 41 magnetic resonance spectra acquired in vivo from two grades of brain tumour, namely low- and medium-grade astrocytic tumours, labelled astrocytomas (AST), and high-grade gliomas and glioblastomas labelled glioblastomas (GL). The aim of this study is threefold. First, to describe the application of the alternative methodologies to MRS, then to benchmark their classification performance, and finally to interpret the classification models in terms of biologically relevant signals derived from the spectra. The classification performance is assessed using the bootstrap method and by application to a test sample in a retrospective study.

Identification and characterisation of childhood cerebellar tumours by in vivo proton MRS

“…However, this is limited by the integrity of the assignments, and potentially valuable information is disregarded if the spectra contain metabolite signals that are not accounted for in the basis set. A comparison of these two different approaches to classification using MRS was beyond the scope of this study; however, future studies should investigate alternative spectral featureextraction methods that do not involve fitting to estimate metabolite concentrations (14,36).…”

“…Potential roles have been identified in pre-surgical diagnosis of tumour type and grade (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19), monitoring of treatment response (20), and evaluation of tumour recurrence (21). However, optimised MRS analysis is required to enable the widespread clinical use of the technique.…”

“…However, optimised MRS analysis is required to enable the widespread clinical use of the technique. Pattern classification of MRS data alone or in combination with MRI features has proven successful for accurately classifying brain tumours (1,(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17). Although brain tumours are the most common solid tumours in children and a major cause of childhood mortality, they are rarer than in adults, and only small numbers have been reported in the few studies using MRS to discriminate paediatric brain tumours (15)(16)(17)(18)(19).…”

Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification

“…Several methods to perform data reduction and feature selection have been described in the literature. 6,[11][12][13] The last step in automated tumor classification, involves the actual classifier. In our case the classifier was based on the Mahalanobis distance.…”

Bayesian statistics in medicine: a 25 year review

“…Tumour grading from magnetic resonance spectroscopy was approached using multivariate Bayesian selection [541]. Prediction of an individual's disease status and population prevalence, using several similar diagnostic tests, used robust Bayesian prediction [542].…”