Classification of tumor area using combined DCE and DSC MRI in patients with glioblastoma

Abstract

This study proposes an automatic method for identification and quantification of different tissue components: the non-enhanced infiltrative tumor, vasogenic edema and enhanced tumor areas, at the subject level, in patients with glioblastoma (GB) based on dynamic contrast enhancement (DCE) and dynamic susceptibility contrast (DSC) MRI. Nineteen MR data sets, obtained from 12 patients with GB, were included. Seven patients were scanned before and 8 weeks following bevacizumab initiation. Segmentation of the tumor area was performed based on the temporal data of DCE and DSC at the group-level using k-means algorithm, and further at the subject-level using support vector machines algorithm. The obtained components were associated to different tissues types based on their temporal characteristics, calculated perfusion and permeability values and MR-spectroscopy. The method enabled the segmentation of the tumor area into the enhancing permeable component; the non-enhancing hypoperfused component, associated with vasogenic edema; and the non-enhancing hyperperfused component, associated with infiltrative tumor. Good agreement was obtained between the group-level, unsupervised and subject-level, supervised classification results, with significant correlation (r = 0.93, p < 0.001) and average symmetric root-mean-square surface distance of 2.5 ± 5.1 mm. Longitudinal changes in the volumes of the three components were assessed alongside therapy. Tumor area segmentation using DCE and DSC can be used to differentiate between vasogenic edema and infiltrative tumors in patients with GB, which is of major clinical importance in therapy response assessment.

Quantitative T1 mapping indicates tumor infiltration beyond the enhancing part of glioblastomas

“…The finding of elevated absolute qT1‐difference values in the edema region stands in line with the results of several previous MRI and MRS studies that also show changes for different MR parameters beyond the enhancing part of glioblastomas: a stripe‐like pattern of regional CBV increase was found in a defined region adjacent to the contrast‐enhancing tumor, which then transformed into enhancing tumor tissue in the follow‐up; the VOI analysis of BOLD‐based relative oxygen extraction fraction (rOEF) maps revealed potentially hypoxic tumor regions with high rOEF in the nonenhancing infiltration zone; peritumoral fiber structures showed significant decreases of N‐acetyl‐aspartate concentrations and fiber densities when compared with the contralateral side . Further studies based on a combination of dynamic contrast enhanced (DCE) and dynamic susceptibility contrast (DSC) MRI or using machine‐learning algorithms on a set of different MRI modalities suggest that these techniques are promising candidates for distinguishing between vasogenic edema and tumor‐containing edema, which appear similar in conventional MRI. The method presented here reveals a new biological aspect of peritumoral alterations in glioblastomas and may thus help with this differentiation, which is important for therapy response assessment.…”

Contrast-enhanced MR Imaging versus Contrast-enhanced US: A Comparison in Glioblastoma Surgery by Using Intraoperative Fusion Imaging

“…Other MR imaging sequences, such as dynamic susceptibility contrast-enhanced imaging, could be more informative with regard to tumor location or invasiveness. Indeed, dynamic susceptibility contrast-enhanced imaging depicts tissue perfusion in regions lacking damage to the blood-brain barrier, encompassing the area shown at T1-weighted gadolinium-enhanced MR imaging (31). It might be used in the future for surgical guidance that also takes into account functional boundaries.…”

Conventional and advanced magnetic resonance imaging in patients with high-grade glioma

“…Both DCE-MRI and DSC-MRI appear to be helpful in identifying areas of tumor infiltration, which is associated with higher perfusion parameters in comparison to vasogenic edema. 48, 49 These metrics may also be useful in identifying areas of tumor infiltration that are at risk of subsequent tumor progression. 50 …”