摘要：

Magnetic resonance imaging (MRI) is the most common and well-established imaging modality for evaluation of intracerebral neoplasms, but there are still some incompletely solved challenges, such as reliable distinction between high- and low-grade tumours, exact delineation of tumour extension, and discrimination between recurrent tumour and radiation necrosis. The aim of this study was to evaluate the contribution of two MRI techniques to non-invasively estimate brain tumour grade. Twenty-four patients referred to MRI examination were analyzed and diagnosed with single intra-axial brain tumour. Lastly, histopathological analysis was performed to verify tumour type. Ten patients presented low-grade gliomas, while the remaining patients showed high-grade tumours, including glioblastomas in eight cases, isolated metastases in four patients and two cases with anaplastic gliomas. MRI examinations were performed on a 1.5-T scanner (Signa, General Electric). The acquisition protocol included the following sequences: saggital T1-weighted localizer, axial T1- and T2-weighted MRI, single-voxel magnetic resonance spectroscopy (MRS), dynamic susceptibility contrast (DSC) MRI and contrast-enhanced T1-weighted MRI. MRS data was analyzed with standard software provided by the scanner manufacturer. The metabolite ratio with the largest significant difference between tumour grades was the choline/creatine (Ch/Cr) ratio with elevated values in high-grade gliomas and metastases. A Ch/Cr ratio equal or larger than 1.55 predicted malignancy grade with 92% sensitivity and 80% specificity. The area under the ROC curve was 0.92 (CI: 95%; 0.81–1). Regarding to perfusion parameters, relative cerebral blood volume (rCBV) maps were estimated from the MR signal intensity time series during bolus passage with two commercial software packages. Two different regions of interest (ROI) were used to evaluate rCBV: lesion centre and perilesional region. All rCBV values were normalized to CBV in a contrallateral normal appearing white matter region. Statistical differences were not found between different tumour types. However, the presence of blood–brain barrier (BBB) damage was illustrated from concentration–time curves calculated in DSC-MRI. A cluster analysis of the time series was used to identify regions with contrast agent extravasation where T1-effects are superimposed to T2*-effects. The presence of BBB damage from concentration–time curves was highly correlated with enhancement of post-contrast T1-weighted images and predicted tumour malignancy with a 92% sensitivity and 90% specificity. A large spatial heterogeneity in concentration–time curves was observed from the cluster analysis, supporting the assumption that ROI selection to compute hemodynamic parameters must be done carefully in order to extract robust parameters.

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