Comparison of Voxel-Wise Tumor Perfusion Changes Measured With Dynamic Contrast-Enhanced (DCE) MRI and Volumetric DCE CT in Patients With Metastatic Brain Cancer Treated with Radiosurgery.

Catherine Coolens,Caroline Chung,Cynthia Menard,Warren Foltz,Brandon Driscoll,Carly Pellow

doi:10.18383/j.tom.2016.00178

Abstract

Dynamic contrast-enhanced (DCE)-MRI metrics are evaluated against volumetric DCE-CT quantitative parameters as a standard for tracer-kinetic validation using a common 4-dimensional temporal dynamic analysis platform in tumor perfusion measurements following stereotactic radiosurgery (SRS) for brain metastases. Patients treated with SRS as part of Research Ethics Board-approved clinical trials underwent volumetric DCE-CT and DCE-MRI at baseline, then at 7 and 21 days after SRS. Temporal dynamic analysis was used to create 3-dimensional pharmacokinetic parameter maps for both modalities. Individual vascular input functions were selected for DCE-CT and a population function was used for DCE-MRI. Semiquantitative and pharmacokinetic DCE parameters were assessed using a modified Tofts model within each tumor at every time point for both modalities for characterization of perfusion and capillary permeability, as well as their dependency on precontrast relaxation times (TRs), T10, and input function. Direct voxel-to-voxel Pearson analysis showed statistically significant correlations between CT and magnetic resonance which peaked at day 7 for Ktrans (R = 0.74, P ≤ .0001). The strongest correlation to DCE-CT measurements was found with DCE-MRI analysis using voxel-wise T10 maps (R = 0.575, P < .001) instead of assigning a fixed T10 value. Comparison of histogram features showed statistically significant correlations between modalities over all tumors for median Ktrans (R = 0.42, P = .01), median area under the enhancement curve (iAUC90) (R = 0.55, P < .01), and median iAUC90 skewness (R = 0.34, P = .03). Statistically significant, strong correlations were found for voxel-wise Ktrans, iAUC90, and ve values between DCE-CT and DCE-MRI. For DCE-MRI, the implementation of voxel-wise T10 maps plays a key role in ensuring the accuracy of heterogeneous pharmacokinetic maps.

Highlights

Dynamic contrast-enhanced (DCE) imaging can be useful for evaluating vascular injury and endothelial permeability changes following radiation therapy, including ablative therapy such as stereotactic radiosurgery (SRS) or when combined with antiangiogenic therapy [1]
Despite voxel-based acquisitions, the analyses were reported in median values and gamma analysis used to assess spatial variation in kinetic parameters in Kallehauge et al This study reports our early clinical experience with tumor perfusion measurements following SRS for brain metastases using both volumetric DCE-computed tomography (CT) and DCE-magnetic resonance imaging (MRI) in the same patients supported by a common temporal dynamic analysis (TDA) framework
The AIFCT curves for patient 1 are shown in Figure 1A, together with the populationbased AIFMRI curve, whereas Figure 1B highlights the variations in input curves for all DCE-CT measurements

Summary

Introduction

Dynamic contrast-enhanced (DCE) imaging can be useful for evaluating vascular injury and endothelial permeability changes following radiation therapy, including ablative therapy such as stereotactic radiosurgery (SRS) or when combined with antiangiogenic therapy [1]. Despite voxel-based acquisitions, the analyses were reported in median values and gamma analysis used to assess spatial variation in kinetic parameters in Kallehauge et al This study reports our early clinical experience with tumor perfusion measurements following SRS for brain metastases using both volumetric DCE-CT and DCE-MRI in the same patients supported by a common TDA framework. It is hypothesized that analyzing contrast enhancement data from both modalities in a unified and voxel-based approach will strengthen the correlations between their parametric output values. This supports the concept that low-molecular-weight contrast agents can help derive tumor permeability and perfusion heterogeneity independent of the imaging modality provided the image analysis methods are standardized

Methods

Results

Conclusion