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Purpose: To grade brain gliomas by using a data-driven analysis of multiparametric magnetic resonance (MR) imaging, taking into account the heterogeneity of the lesions at MR imaging, and to compare these results with the most widespread current radiologic reporting methods., Materials and Methods: One hundred eighteen patients with histologically confirmed brain gliomas were evaluated retrospectively. Conventional and advanced MR sequences (perfusion-weighted imaging, MR spectroscopy, and diffusion-tensor imaging) were performed. Three evaluations were conducted: semiquantitative (based on conventional and advanced sequences with reported cutoffs), qualitative (exclusively based on conventional MR imaging), and quantitative. For quantitative analysis, four volumes of interest were placed: regions with contrast material enhancement, regions with highest and lowest signal intensity on T2-weighted images, and regions of most restricted diffusivity. Statistical analysis included t test, receiver operating characteristic (ROC) analysis, discriminant function analysis (DFA), leave-one-out cross-validation, and Kendall coefficient of concordance., Results: Significant differences were noted in age, relative cerebral blood volume (rCBV) in contrast-enhanced regions (cutoff > 2.59; sensitivity, 80%; specificity, 91%; area under the ROC curve [AUC] = 0.937; P = .0001), areas of lowest signal intensity on T2-weighted images (>2.45, 57%, 97%, 0.852, and P = .0001, respectively), restricted diffusivity regions (>2.61, 54%, 97%, 0.808, and P = .0001, respectively), and choline/creatine ratio in regions with the lowest signal intensity on T2-weighted images (>2.07, 49%, 88%, 0.685, and P = .0007, respectively). DFA that included age; rCBV in contrast-enhanced regions, areas of lowest signal intensity on T2-weighted images, and areas of restricted diffusivity; and choline/creatine ratio in areas with lowest signal intensity on T2-weighted images was used to classify 95% of patients correctly. Quantitative analysis showed a higher concordance with histologic findings than qualitative and semiquantitative methods (P < .0001)., Conclusion: A quantitative multiparametric MR imaging evaluation that incorporated heterogeneity at MR imaging significantly improved discrimination between low- and high-grade brain gliomas with a very high AUC (ie, 0.95), thus reducing the risk of inappropriate or delayed surgery, respectively.

Background and Purpose: fcMRI measures spontaneous and synchronous fluctuations of BOLD signal between spatially remote brain regions. The present study investigated potential LN fcMRI modifications induced by left hemisphere brain gliomas., Materials and Methods: We retrospectively evaluated fcMRI in 39 right-handed patients with a left hemisphere brain glioma and 13 healthy controls. Patients and controls performed a verb-generation task to identify individual BOLD activity in the left IFG (Broca area); the active region was used as seed to create whole-brain background connectivity maps and to identify the LN (including bilateral regions of the IFG, STS, and TPJ) following regression of task-evoked activity. We assessed differences between patients and controls in the pattern of functional connectivity of the LN, as well as potential effects of tumor position, histopathology, and volume., Results: Global fcMRI of the LN was significantly reduced in patients with tumor compared with controls. Specifically, fcMRI was significantly reduced within seed regions of the affected hemisphere (left intrahemispheric fcMRI) and between the TPJ of the 2 hemispheres. In patients, the left TPJ node showed the greatest decrease of functional connectivity within the LN., Conclusions: The presence of a brain tumor in the left hemisphere significantly reduced the degree of fcMRI between language-related brain regions. The pattern of fcMRI was influenced by tumor position but was not restricted to the area immediately surrounding the tumor because the connectivity between remote and contralateral areas was also affected.

Purpose: The aim of the study was to evaluate connectivity modifications in the Default Mode Network (DMN) in patients with cerebral glioma, and to correlate these modifications to tumor characteristics., Methods: Twenty-four patients with a left-hemisphere cerebral tumor (14 grade II and 10 grade IV gliomas) and 14 healthy age-matched right-hand volunteers were enrolled in the study. Subjects underwent fMRI while performing language tasks for presurgical mapping. Data was analyzed with independent component analysis in order to identify the DMN. DMN group maps were produced by random-effect analysis (p<0.001, FDR-corrected). An analysis of variance across the three groups (p<0.05) and post-hoc t-test contrasts between pairs of groups were calculated (p<0.05, FDR-corrected)., Results: All three groups showed typical DMN areas. However, reduced DMN connectivity was detected in tumor patients with respect to controls. A significantly increased and reduced integration of DMN areas was observed in the hippocampal and prefrontal regions, respectively. Modifications were closely related to tumor grading. Moreover, the DMN lateralized to the hemisphere contralateral to tumor in the low-grade, but not in the high-grade tumor patients., Conclusion: Modifications of DMN connectivity were induced by gliomas and differed for high and low grade tumors.

Background and Purpose: An alternative technique, which is less influenced by tumor- and patient-related factors, is required to overcome the limits of GLM analysis of fMRI data in patients. The aim of this study was to statistically assess differences in the identification of language regions and hemispheric lateralization of language function between controls and patients as estimated by both the GLM and a novel combined ICA-GLM procedure., Materials and Methods: We retrospectively evaluated 42 patients with pathologically confirmed brain gliomas of the left frontal and/or temporoparietal lobes and a control group of 14 age-matched healthy volunteers who underwent BOLD fMRI to lateralize language functions in the cerebral hemispheres. Data were processed by using a classic GLM and ICA-GLM., Results: ICA-GLM demonstrated a higher sensitivity in detecting language activation, specifically in the left TPJ of patients. There were no significant differences between the GLM and ICA-GLM in controls; however, statistically significant differences were observed by using ICA-GLM for the LI in patients. For the computation of the LI, ICA-GLM was less influenced by the chosen statistical threshold compared with the GLM., Conclusions: We suggest the use of the ICA-GLM as a valid alternative to the classic GLM method for presurgical mapping in patients with brain tumors and to replicate the present results in a broader sample of patients.