Dynamic contrast-enhanced imaging and analysis at high spatial resolution of MCF7 human breast tumors

Abstract

High resolution, dynamic GdDTPA-enhanced images of MCF7 human breast tumors in immunodeficient mice were analyzed at pixel resolution. The analysis, based on a physiological model, was performed by applying a nonlinear least-square algorithm using a color coded scale. The final output mapped at pixel resolution capillary permeability times surface area and fraction of extracellular volume, for each tumor slice. In addition, the output included assessment of the fit to the model by determining the proportion of variability (R2) for each pixel. The spatial variation in the R2 values served to identify regions where the predominant mechanism of enhancement was leakage from the intravascular volume to the extracellular volume (R2 close to 1). In regions with low R2 other mechanisms of enhancement appear to be dominating presumably diffusion within the extracellular space. As expected, in necrotic regions lacking microcapillaries and identified by analyzing T2-weighted images of the same tumors, the model failed to fit the dynamic contrast enhanced data. The heterogeneous distribution of the determined pathophysiological features demonstrates the importance of recording and analyzing breast tumor images at high spatial resolution.