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

• Autor(a) principal: Cuellar-Baena,S.
• Data de Publicação: 2011
• Outros Autores: Morais,L.M.T.S., Cendes,F., Faria,A.V., Castellano,G.
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Brazilian Journal of Medical and Biological Research
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2011000400012
• topic: Proton magnetic resonance spectroscopy; Brain tumors; AMARES; Tumor classification

In vivo proton magnetic resonance spectroscopy (¹H-MRS) is a technique capable of assessing biochemical content and pathways in normal and pathological tissue. In the brain, ¹H-MRS complements the information given by magnetic resonance images. The main goal of the present study was to assess the accuracy of ¹H-MRS for the classification of brain tumors in 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 by two methods: a manual one consisting of integration of manually defined peak areas, and the advanced method for accurate, robust and efficient spectral fitting (AMARES), a semi-automatic quantification method implemented in the jMRUI software. Statistical methods included discriminant analysis and the leave-one-out cross-validation method. Both manual and semi-automatic analyses detected differences in metabolite content between 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 methods classified all control subjects correctly. The study demonstrated that ¹H-MRS accurately differentiated normal from tumoral brain tissue and confirmed the superiority of the semi-automatic quantification method.