Diagnostic utility of Restriction Spectrum Imaging in the characterization of the peritumoral brain zone in glioblastoma: Analysis of overall and progression-free survival.

PURPOSE: We studied the ability of Restriction Spectrum Imaging (RSI), a novel advanced diffusion imaging technique, to estimate levels of cellularity in different glioblastoma regions, evaluated their prognostic value compared with established clinical diffusion metrics such as fractional anisotropy (FA) and mean diffusivity (MD). METHODS: Forty-two patients with untreated glioblastoma, IDH-wildtype, were examined with an advanced MRI tumor protocol. The region of interest (ROI) was obtained from the contrast-enhancing part of tumor and the peritumoral brain zones and then co-registered with RSI-cellularity index, FA and MD maps. Histogram parameters of diffusion metrics were assessed for all ROI locations and compared to MGMT promoter methylation status and survival. The ability of RSI-cellularity index, FA, and MD to stratify survival and were assessed by Cox proportional hazard regression, adjusted for significant clinical predictors. RESULTS: The highest RSI-cellularity index was measured in contrast-enhancing tumor core with a negative gradient from tumor core to the periphery of peritumoral zone with predictive accuracy 81 % (P < 0.001). Shorter overall survival was significant associated with higher RSI-cellularity index (hazard ratio (HR) 3.6, 95 % confidence interval (CI) 1.3-9.5, P = 0.002) with synchronal decrease in MD (HR 0.31, 95 %CI 0.1-0.8, P = 0.008) in the contrast-enhanced tumor core. This association was also consistent for RSI-cellularity index value measured in the peri-enhancing zone (HR 3.6, 95 % CI 1.0-12.3, P = 0.041). No statistically significant differences were noted between RSI-cellularity index, FA, nor MD and MGMT promoter methylation. CONCLUSION: RSI-cellularity index may be used as prognostic biomarker to improve risk stratification in patients with glioblastoma.

Prediction of survival and progression in glioblastoma patients using temporal perfusion changes during radiochemotherapy.

BACKGROUND: The aim of this study was to investigate changes in structural magnetic resonance imaging (MRI) according to the RANO criteria and perfusion- and permeability related metrics derived from dynamic contrast-enhanced MRI (DCE) and dynamic susceptibility contrast MRI (DSC) during radiochemotherapy for prediction of progression and survival in glioblastoma. METHODS: Twenty-three glioblastoma patients underwent biweekly structural and perfusion MRI before, during, and two weeks after a six weeks course of radiochemotherapy. Temporal trends of tumor volume and the perfusion-derived parameters cerebral blood volume (CBV) and blood flow (CBF) from DSC and DCE, in addition to contrast agent capillary transfer constant (Ktrans) from DCE, were assessed. The patients were separated in two groups by median survival and differences between the two groups explored. Clinical- and MRI metrics were investigated using univariate and multivariate survival analysis and a predictive survival index was generated. RESULTS: Median survival was 19.2 months. A significant decrease in contrast-enhancing tumor size and CBV and CBF in both DCE- and DSC-derived parameters was seen during and two weeks past radiochemotherapy (p < 0.05). A 10%/30% increase in Ktrans/CBF two weeks after finishing radiochemotherapy resulted in significant shorter survival (13.9/16.8 vs. 31.5/33.1 months; p < 0.05). Multivariate analysis revealed an index using change in Ktrans and relative CBV from DSC significantly corresponding with survival time in months (r2 = 0.843; p < 0.001). CONCLUSIONS: Significant temporal changes are evident during radiochemotherapy in tumor size (after two weeks) and perfusion-weighted MRI-derived parameters (after four weeks) in glioblastoma patients. While DCE-based metrics showed most promise for early survival prediction, a multiparametric combination of both DCE- and DSC-derived metrics gave additional information.

Diffusion tensor and restriction spectrum imaging reflect different aspects of neurodegeneration in Parkinson's disease.

To meet the need for Parkinson's disease biomarkers and evidence for amount and distribution of pathological changes, MRI diffusion tensor imaging (DTI) has been explored in a number of previous studies. However, conflicting results warrant further investigations. As tissue microstructure, particularly of the grey matter, is heterogeneous, a more precise diffusion model may benefit tissue characterization. The purpose of this study was to analyze the diffusion-based imaging technique restriction spectrum imaging (RSI) and DTI, and their ability to detect microstructural changes within brain regions associated with motor function in Parkinson's disease. Diffusion weighted (DW) MR images of a total of 100 individuals, (46 Parkinson's disease patients and 54 healthy controls) were collected using b-values of 0-4000s/mm2. Output diffusion-based maps were estimated based on the RSI-model combining the full set of DW-images (Cellular Index (CI), Neurite Density (ND)) and DTI-model combining b = 0 and b = 1000 s/mm2 (fractional anisotropy (FA), Axial-, Mean- and Radial diffusivity (AD, MD, RD)). All parametric maps were analyzed in a voxel-wise group analysis, with focus on typical brain regions associated with Parkinson's disease pathology. CI, ND and DTI diffusivity metrics (AD, MD, RD) demonstrated the ability to differentiate between groups, with strongest performance within the thalamus, prone to pathology in Parkinson's disease. Our results indicate that RSI may improve the predictive power of diffusion-based MRI, and provide additional information when combined with the standard diffusivity measurements. In the absence of major atrophy, diffusion techniques may reveal microstructural pathology. Our results suggest that protocols for MRI diffusion imaging may be adapted to more sensitive detection of pathology at different sites of the central nervous system.

Demonstration of Non-Gaussian Restricted Diffusion in Tumor Cells Using Diffusion Time-Dependent Diffusion-Weighted Magnetic Resonance Imaging Contrast.

The diffusion-weighted magnetic resonance imaging (DWI) technique enables quantification of water mobility for probing microstructural properties of biological tissue and has become an effective tool for collecting information about the underlying pathology of cancerous tissue. Measurements using multiple b-values have indicated biexponential signal attenuation, ascribed to "fast" (high ADC) and "slow" (low ADC) diffusion components. In this empirical study, we investigate the properties of the diffusion time (Δ)-dependent components of the diffusion-weighted (DW) signal in a constant b-value experiment. A xenograft gliobastoma mouse was imaged using Δ = 11 ms, 20 ms, 40 ms, 60 ms, and b = 500-4000 s/mm(2) in intervals of 500 s/mm(2). Data were corrected for EPI distortions, and the Δ-dependence on the DW-signal was measured within three regions of interest [intermediate- and high-density tumor regions and normal-appearing brain (NAB) tissue regions]. In this study, we verify the assumption that the slow decaying component of the DW-signal is non-Gaussian and dependent on Δ, consistent with restricted diffusion of the intracellular space. As the DW-signal is a function of Δ and is specific to restricted diffusion, manipulating Δ at constant b-value (cb) provides a complementary and direct approach for separating the restricted from the hindered diffusion component. We found that Δ-dependence is specific to the tumor tissue signal. Based on an extended biexponential model, we verified the interpretation of the diffusion time-dependent contrast and successfully estimated the intracellular restricted ADC, signal volume fraction, and cell size within each ROI.

Serial diffusion tensor imaging for early detection of radiation-induced injuries to normal-appearing white matter in high-grade glioma patients.

PURPOSE: To study the potential of diffusion tensor imaging (DTI) to serve as a biomarker for radiation-induced brain injury during chemo-radiotherapy (RT) treatment. MATERIALS AND METHODS: Serial DTI data were collected from 18 high-grade glioma (HGG) patients undergoing RT and 7 healthy controls. Changes across time in mean, standard deviation (SD), skewness, and kurtosis of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (λa ), and transversal diffusivity (λt ) within the normal-appearing white matter (NAWM) were modeled using a linear mixed-effects model to assess dose dependent changes of five dose bins (0-60 Gy), and global changes compared with a control group. RESULTS: Mean MD, λa and λt were all significantly increasing in >41 Gy dose regions (0.14%, 0.10%, and 0.18% per week) compared with <12 Gy regions. SD λt had significant dose dependent time evolution of 0.019*dose per week. Mean and SD MD, λa and λt in the global NAWM of the patient group significantly increased (mean; 0.06%, 0.03%, 0.09%, and SD; 0.57%, 0.34%, 0.51 per week) compared with the control group. The changes were significant at week 6 of, or immediately after RT. CONCLUSION: DTI is not sensitive to acute global NAWM changes during the treatment of HGG, but sensitive to early posttreatment changes.