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

Vascular endothelial growth factor (VEGF) is a receptor ligand that promotes neovascularization,1,2 allowing tumor growth3 and metastatic dissemination. Tumor neovasculature is structurally and functionally abnormal when compared with normal vasculature.4 This tortuous, dilated, and poorly organized tumor vascularity is hyperpermeable, which in turn raises tumor interstitial fluid pressure5 and compromises delivery of both ultrasound contrast agents (UCAs) and chemotherapeutic agents. Because VEGF-driven angiogenesis is a process critical in the early stages of tumor development, anti-VEGF treatments are ideally administered early in cancer therapy to improve progression-free survival.6 In addition to preventing angiogenesis, inhibition of the VEGF signaling pathway increases effectiveness of combinational therapies likely through normalization of the tumor vasculature network and reduction of tumor interstitial pressure,4,7 thereby allowing improved delivery of chemotherapeutic drugs.8 Bevacizumab is a recombinant humanized monoclonal antibody to VEGF and has emerged as a promising treatment option for the management of breast cancer and other cancer types.6,9–12 Unfortunately, not all patients respond to bevacizumab therapy.13 It has been proposed that bevacizumab drug resistance and continued tumor growth may be due to the advanced state of tumor development whereby compensatory mechanisms for angiogenesis have emerged.1,14 Selection of only those patients likely to benefit from bevacizumab therapy would be ideal, but no proven predictive factors for VEGF-targeted therapy have been identified to date. Traditional gray scale ultrasound imaging is one of the most frequently used clinical imaging modalities. The primary advantages of this imaging method are minimal health risks associated with patient exposure to ultrasonic energy, real-time capability, and the relatively low cost associated with both examinations and system maintenance. A recent advance in ultrasound imaging has been the development of UCAs. These microbubbles are subcapillary sized gas-filled spheres. The use of a low-diffusivity gas and a thin stabilizing shell (lipid or protein based) permits a circulatory lifetime on the order of tens of minutes. Because of a large acoustical impedance difference between intravascular UCAs and the surrounding blood, they are excellent ultrasound scatterers. Ultrasound contrast agents, therefore, improve ultrasound sensitivity to slow blood flow in small vessels such as those associated with tumor neovasculature. In response to an ultrasonic field, UCAs undergo an oscillatory movement (or resonate), a process called stable cavitation. Under sufficiently high ultrasonic pressures (50–200 kPa), this can lead to nonlinear scattering and generation of echoes with frequency components at multiples of the transmit frequency.15 These harmonic signals can be selectively isolated from the ultrasonic signals using various signal-processing and pulsing techniques, forming the basis of many contrast-enhanced ultrasound (CEUS) imaging techniques. Because these signals are less pronounced in tissue, harmonic-based CEUS imaging techniques yield improved microbubble-to-tissue contrast levels. In fact, power modulation (or pulse inversion) harmonic imaging is a relatively new technique with demonstrated improved image contrast over other ultrasound-based fundamental (ie, traditional gray scale) and harmonic imaging strategies.16 Moreover, power modulation harmonic imaging (PMHI) takes advantage of the unique nonlinear resonant behavior of UCAs for further improvements in microbubble detection sensitivity.17 The ability to visualize UCAs traversing tumor vasculature introduces a unique opportunity for quantifying tumor perfusion and flow kinetics. Because UCAs are sensitive to ultrasound exposure, insonation at high mechanical index (MI) levels is known to cause rapid microbubble destruction.18,19 Therefore, UCAs within an imaging field can be destroyed by a short sequence of high-MI (>0.8) pulses followed by visualization of UCA replenishment within that tissue region using low-MI (<0.2) ultrasound imaging. This destruction-replenishment technique allows analysis of the resultant time-intensity curves derived from a sequence of ultrasound images after microbubble destruction.20,21 An artificial neural network (ANN) is a nonlinear modeling technique inspired by the information-processing and learning behavior of biological nervous systems. In the past, neural networks have been used in conjunction with medical image analysis (findings) to categorize input patterns into 2 or more classes, such as benignity or malignancy, of breast22,23 and prostate24 tumors. By using histologic findings (eg, microvessel density [MVD]) as an objective measure (reference standard) of the breast cancer response or no response to bevacizumab therapy, ultrasound-based tumor metrics may be formulated in a neural network context to reveal patterns suggestive of therapeutic efficacy. In this study, we investigate the feasibility of using CEUS for characterizing blood flow in a breast cancer xenograft model. Combined with ANN data modeling and trend recognition capability, a method for determining the breast cancer response to bevacizumab is introduced and analyzed.