Determination of Breast Cancer Response to Bevacizumab Therapy Using Contrast‐Enhanced Ultrasound and Artificial Neural Networks

Objective . The purpose of this study was to evaluate contrast‐enhanced ultrasound and neural network data classification for determining the breast cancer response to bevacizumab therapy in a murine model. Methods . An ultrasound scanner operating in the harmonic mode was used to measure ultrasound contrast agent (UCA) time‐intensity curves in vivo. Twenty‐five nude athymic mice with orthotopic breast cancers received a 30‐μL tail vein bolus of a perflutren microsphere UCA, and baseline tumor imaging was performed using microbubble destruction‐replenishment techniques. Subsequently, 15 animals received a 0.2‐mg injection of bevacizumab, whereas 10 control animals received an equivalent dose of saline. Animals were reimaged on days 1, 2, 3, and 6 before euthanasia. Histologic assessment of excised tumor sections was performed. Time‐intensity curve analysis for a given region of interest was conducted using customized software. Tumor perfusion metrics on days 1, 2, 3, and 6 were modeled using neural network data classification schemes (60% learning and 40% testing) to predict the breast cancer response to therapy. Results . The breast cancer response to a single dose of bevacizumab in a murine model was immediate and transient. Permutations of input to the neural network data classification scheme revealed that tumor perfusion data within 3 days of bevacizumab dosing was sufficient to minimize the prediction error to 10%, whereas measurements of physical tumor size alone did not appear adequate to assess the therapeutic response. Conclusions . Contrast‐enhanced ultrasound may be a useful tool for determining the response to bevacizumab therapy and monitoring the subsequent restoration of blood flow to breast cancer.