DCE-MRI biomarkers of tumour heterogeneity predict CRC liver metastasis shrinkage following bevacizumab and FOLFOX-6

Background: There is limited evidence that imaging biomarkers can predict subsequent response to therapy. Such prognostic and/or predictive biomarkers would facilitate development of personalised medicine. We hypothesised that pre-treatment measurement of the heterogeneity of tumour vascular enhancement could predict clinical outcome following combination anti-angiogenic and cytotoxic chemotherapy in colorectal cancer (CRC) liver metastases. Methods: Ten patients with 26 CRC liver metastases had two dynamic contrast-enhanced MRI (DCE-MRI) examinations before starting first-line bevacizumab and FOLFOX-6. Pre-treatment biomarkers of tumour microvasculature were computed and a regression analysis was performed against the post-treatment change in tumour volume after five cycles of therapy. The ability of the resulting linear model to predict tumour shrinkage was evaluated using leave-one-out validation. Robustness to inter-visit variation was investigated using data from a second baseline scan. Results: In all, 86% of the variance in post-treatment tumour shrinkage was explained by the median extravascular extracellular volume (ve), tumour enhancing fraction (EF), and microvascular uniformity (assessed with the fractal measure box dimension, d0) (R2=0.86, P<0.00005). Other variables, including baseline volume were not statistically significant. Median prediction error was 12%. Equivalent results were obtained from the second scan. Conclusion: Traditional image analyses may over-simplify tumour biology. Measuring microvascular heterogeneity may yield important prognostic and/or predictive biomarkers

Quantifying heterogeneity in dynamic contrast-enhanced MRI parameter maps

Simple summary statistics of Dynamic Contrast-Enhanced MRI (DCE-MRI) parameter maps (e.g. the median) neglect the spatial arrangement of parameters, which appears to carry important diagnostic and prognostic information. This paper describes novel statistics that are sensitive to both parameter values and their spatial arrangement. Binary objects are created from 3-D DCE-MRI parameter maps by “extruding” each voxel into a fourth dimension; the extrusion distance is proportional to the voxel’s value. The following statistics are then computed on these 4-D binary objects: surface area, volume, surface area to volume ratio, and box counting (fractal) dimension. An experiment using 4 low and 5 high grade gliomas showed significant differences between the two grades for box counting dimension computed for extruded v e maps, surface area of extruded K trans and v e maps and the volume of extruded v e maps (all p < 0.05). An experiment using 18 liver metastases imaged before and after treatment with a vascular endothelial growth factor (VEGF) inhibitor showed significant differences for surface area to volume ratio computed for extruded K trans and v e maps (p = 0.0013 and p = 0.045 respectively)

Quantifying spatial heterogeneity in dynamic contrast-enhanced MRI parameter maps

Dynamic contrast-enhanced MRI is becoming a standard tool for imaging-based trials of anti-vascular/angiogenic agents in cancer. So far, however, biomarkers derived from DCE-MRI parameter maps have largely neglected the fact that the maps have spatial structure and instead focussed on distributional summary statistics. Such statistics—e.g., biomarkers based on median values—neglect the spatial arrangement of parameters, which may carry important diagnostic and prognostic information. This article describes two types of heterogeneity biomarker that are sensitive to both parameter values and their spatial arrangement. Methods based on Rényi fractal dimensions and geometrical properties are developed, both of which attempt to describe the complexity of DCE-MRI parameter maps. Experiments using simulated data show that the proposed biomarkers are sensitive to changes that distribution-based summary statistics cannot detect and demonstrate that heterogeneity biomarkers could be applied in the drug trial setting. An experiment using 23 DCE-MRI parameter maps of gliomas—a class of tumour that is graded on the basis of heterogeneity—shows that the proposed heterogeneity biomarkers are able to differentiate between low- and high-grade tumour