Unified platform for multimodal voxel-based analysis to evaluate tumour perfusion and diffusion characteristics before and after radiation treatment evaluated in metastatic brain cancer

Abstract

Objective:: Early changes in tumour behaviour following stereotactic radiosurgery) are potential biomarkers of response. To-date quantitative model-based measures of dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) MRI parameters have shown widely variable findings, which may be attributable to variability in image acquisition, post-processing and analysis. Big data analytic approaches are needed for the automation of computationally intensive modelling calculations for every voxel, independent of observer interpretation.

Methods:: This unified platform is a voxel-based, multimodality architecture that brings complimentary solute transport processes such as perfusion and diffusion into a common framework. The methodology was tested on synthetic data and digital reference objects and consequently evaluated in patients who underwent volumetric DCE-CT, DCE-MRI and DWI-MRI scans before and after treatment. Three-dimensional pharmacokinetic parameter maps from both modalities were compared as well as the correlation between apparent diffusion coefficient (ADC) values and the extravascular, extracellular volume (V_e). Comparison of histogram parameters was done via Bland-Altman analysis, as well as Student's t-test and Pearson's correlation using two-sided analysis.

Results:: System testing on synthetic Tofts model data and digital reference objects recovered the ground truth parameters with mean relative percent error of 1.07 × 10^-7 and 5.60 × 10^-4 respectively. Direct voxel-to-voxel Pearson's analysis showed statistically significant correlations between CT and MR which peaked at Day 7 for K_trans (R = 0.74, p <= 0.0001). Statistically significant correlations were also present between ADC and Ve derived from both DCE-MRI and DCE-CT with highest median correlations found at Day 3 between median ADC and Ve,_MRI values (R = 0.6, p < 0.01) The strongest correlation to DCE-CT measurements was found with DCE-MRI analysis using voxelwise T₁₀ maps (R = 0.575, p < 0.001) instead of assigning a fixed T₁₀ value.

Conclusion:: The unified implementation of multiparametric transport modelling allowed for more robust and timely observer-independent data analytics. Utility of a common analysis platform has shown higher correlations between pharmacokinetic parameters obtained from different modalities than has previously been reported.

Advances in knowledge:: Utility of a common analysis platform has shown statistically higher correlations between pharmacokinetic parameters obtained from different modalities than has previously been reported.

Figure 1.

Schematic overview of the workflow of the standardized analysis platform. ADC, apparent diffusion coefficient; TDA, temporal dynamic analysis.

Figure 2.

Comparison plots of (a) mean K_trans values, (b) K_trans histogram skewness and (c) K_trans histogram kurtosis values obtained with DCE-CT and DCE-MRI per tumour.

Figure 3.

(a) ADC, K_trans, V_e, AUC and T1-Gad images Pre-SRS and Day 20 post-SRS for a typical tumour; (b) Histogram analysis for the same tumour and parameters. ADC, apparent diffusion coefficient; AUC, area under the curve; SRS, stereotactic radio surgery.