Compound speckle model detects anti-angiogenic tumor response in preclinical nonlinear contrast-enhanced ultrasonography.

Abstract

This paper proposes a method for analyzing the first-order speckle statistics of nonlinear contrast-enhanced ultrasound images from tumors. Contrast signal intensity is modeled as a compound distribution of exponential probability density functions with a gamma weighting function. The gamma probability weighting function serves as an approximation for log-normally distributed flow velocities in a vascular network. The model was applied to sub-harmonic bolus-injection images acquired from a mouse breast cancer xenograft model treated with murine version bevacizumab. The area under curve produced using the compound statistical model could more accurately discriminate anti-VEGF-treated tumors from untreated tumors than conventional contrast-enhanced ultrasound image processing. This result was validated with gold standard histological measures of microvascular density. Fractal vessel geometry was estimated using the gamma weighting function and tested against micro-CT perfusion casting. Treated tumors had a significantly lower vascular fractal dimension than control tumors. Vascular complexity estimated using the ultrasound compound statistical model performed similarly to micro-CT fractal dimension for discriminating treated from control tumors. The proposed technique can quantify tumor perfusion and provide an index of vascular complexity, making it a potentially useful addition for clinical detection of vascular normalization in anti-angiogenic trials.