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-microL 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.
Hoyt
Kenneth
K
Department of Radiology and Comprehensive Cancer Center, University of Alabama, Birmingham, Alabama, USA. hoyt@uab.edu
Warram
Jason M
JM
Umphrey
Heidi
H
Belt
Lin
L
Lockhart
Mark E
ME
Robbin
Michelle L
ML
Zinn
Kurt R
KR
D000818:Animals; D000911:Antibodies, Monoclonal / Q000494:pharmacology*; D061067:Antibodies, Monoclonal, Humanized; D000068258:Bevacizumab; D003287:Contrast Media; D005260:Female; D005466:Fluorocarbons; D015674:Mammary Neoplasms, Animal / Q000000981:diagnostic imaging* / Q000188:drug therapy*; D051379:Mice; D008819:Mice, Nude; D016571:Neural Networks, Computer; D018084:Ultrasonography, Interventional; D016217:Ultrasonography, Mammary
D000911:Antibodies, Monoclonal; D061067:Antibodies, Monoclonal, Humanized; D003287:Contrast Media; D005466:Fluorocarbons; D000068258:Bevacizumab; C042852:perflutren