Differentiation of Recurrent Glioblastoma Multiforme from Radiation Necrosis after External Beam Radiation Therapy with Dynamic Susceptibility-weighted Contrast-enhanced Perfusion MR Imaging

Abstract

These findings suggest that DSC perfusion MR imaging may be used to differentiate recurrent GBM from EBRT-induced radiation necrosis.

Pseudoprogression, radionecrosis, inflammation or true tumor progression? challenges associated with glioblastoma response assessment in an evolving therapeutic landscape

“…Some studies conclude that specific relative thresholds are helpful, with lesion-to-normal white matter rCBV ratio thresholds ranging from 1.2 to 2.0 for delineation of pseudoprogression (low rCBV) from recurrent disease (high rCBV) [35][36][37][38]. This is supported by studies in brain metastases demonstrating high reliability of detecting tumor progression (high rCBV) from radionecrosis (low rCBV) using a lesion to normal white matter rCBV ratio higher than 1.5-2.0 [39,40] and studies showing that rCBV can reliably differentiate radionecrosis from true progression in malignant gliomas [38,41]. A significant issue with this simple dichotomy of "true progression" and "pseudoprogression" is that in practice, when progressive enhancement is detected, tumor often coexists with treatment-related enhancement.…”

Perfusion CT Imaging of Brain Tumors: An Overview

“…Kamiryo et al 32 also demonstrated that BBB architecture of capillaries within previously irradiated brain tissue remains intact despite a decrease in the mean capillary attenuation as well as increased capillary diameter. Barajas et al 33 showed lower relative percentage signal recovery (rPSR) in recurrent glioblastoma multiforme compared with radiation necrosis by using DSC-MR perfusion imaging, suggesting a disrupted BBB that was more permeable to macromolecular contrast agents; however, their measurements were not a direct estimate of lesion leakiness. They also noted a large degree of overlap between the 2 groups, making rPSR a less robust predictor of recurrent tumor.…”

Classification of High-Grade Glioma into Tumor and Nontumor Components Using Support Vector Machine

“…Classification into tumor and nontumor components was performed based on conventional parameters (nT1WIϩGd, and nFLAIR) and parameters extracted from DCE (v p , K trans , and BAT). While perfusion parameters can be obtained using dynamic susceptibility contrast imaging and have been shown to differentiate between tumor and nontumor components, [11][12][13]35,36 DCE imaging is preferable due to its higher spatial resolution, less sensitivity to susceptibility artifacts, and provision of quantitative parameters, including permeability (K trans ).…”

“…Hemodynamic parameters, including plasma volume (v p ) and tissue transfer constants (specifically, volume transfer constant [K trans ] and interstitium-to-plasma rate constant [k ep ]), can be extracted from DCE data. Several studies have suggested the utility of DCE parameters as important imaging markers for brain tumor diagnosis, grading, and therapy-response monitoring [7][8][9] ; to differentiate between active and nonactive tumor components [10][11][12][13][14] or infiltrative and noninfiltrative tumor 10,15 ; and for early-stage assessment of antiangiogenic therapy response in HGG. 16,17 However, none of these studies have aimed to classify the entire lesion area, both enhancing and nonenhancing, into tumor and nontumor areas at the voxel level.…”