Manual and semi-automatic quantification of in vivo 1H-MRS data for classification of human primary brain tumors

Abstract

In vivo Proton Magnetic Resonance Spectroscopy (1H-MRS) is an approach capable of assessing biochemical content and pathways developed in normal and pathological tissue. In the brain, 1H-MRS complements the information given by magnetic resonance images. The main goal of this work was to assess the accuracy of 1H-MRS for the classification of brain tumors through a pilot study comparing results obtained by manual and semi-automatic quantification of metabolites. In vivo single-voxel 1H-MRS was performed in 24 control subjects and 26 patients with brain neoplasms that included meningiomas, high grade neuroglial tumors and pilocytic astrocytomas. Seven metabolite groups (lactate, lipids, N-acetyl-aspartate, glutamate and glutamine group, total creatine, total choline, myo-inositol) were evaluated in all spectra using two methods: the manual method, consisting of integration of manually defined peak areas, and the AMARES (Advanced Method for Accurate, Robust and Efficient Spectral fitting) quantification method, a semi-automatic method implemented in the jMRUI software. Statistical methods included Analysis of Variance (ANOVA), Discriminant Analysis (LDA) and Leave-one-out (LOOCV) cross validation method. Both manual and semi-automatic analyses showed differences in metabolite amounts among tumor groups and controls (p < 0.005). The classification accuracy obtained with the manual method was 75% for high grade neuroglial tumors, 55% for meningiomas and 56% for pilocytic astrocytomas, while for the semi-automatic method it was 78%, 70% and 98% respectively. Both the methods obtained 100% for control subjects. This study confirmed that 1H-MRS is accurate in differentiating normal and tumoral brain tissue and confirmed the superiority of the semi-automatic quantification method. (AU)