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

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.

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

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 percent for high-grade neuroglial tumors, 55 percent for meningiomas and 56 percent for pilocytic astrocytomas, while for the semi-automatic method it was 78, 70, and 98 percent, 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.

Comparative metabolic profiling of paediatric ependymoma, medulloblastoma and pilocytic astrocytoma.

Brain tumours are the most common solid tumours in children and a major cause of childhood mortality. The most common paediatric brain tumours include ependymomas, cerebellar astrocytomas and medulloblastomas. These brain tumours are highly heterogeneous regarding their histology, prognosis and therapeutic response. Subtle biochemical changes can be detected in intact tissues by High-Resolution Proton Magnetic Angle Spinning Spectroscopy (HR-MAS) revealing the status of tumour microheterogeneity and metabolic alterations before they are morphologically detectable. In this study, we present metabolic profiles by HR-MAS of 20 intact tissue samples from paediatric brain tumours. Tumour types include ependymoma, medulloblastoma and pilocytic astrocytoma. The metabolic characterization of paediatric brain tumour tissue by HR-MAS spectroscopy provided differential patterns for these tumours. The metabolic composition of the tumour tissue was highly consistent with previous in vivo and ex vivo studies. Some resonances detected in this work and not previously observed by in vivo spectroscopy also show potential in determining tumour type and grade (fatty acids, phenylalanine, glutamate). Overall, this work suggests that the additional information obtained by NMR metabolic profiling applied to tissue from paediatric brain tumours may be useful for assessing tumour grade and determining optimum treatment strategies.