title: Determination of breast cancer response to bevacizumab therapy using contrast-enhanced ultrasound and artificial neural networks.
abstract: 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.
authors.affiliation: Department of Radiology and Comprehensive Cancer Center, University of Alabama, Birmingham, Alabama, USA. hoyt@uab.edu
authors.forename: Kenneth
authors.initials: K
authors.lastname: Hoyt
authors.forename: Jason M
authors.initials: JM
authors.lastname: Warram
authors.forename: Heidi
authors.initials: H
authors.lastname: Umphrey
authors.forename: Lin
authors.initials: L
authors.lastname: Belt
authors.forename: Mark E
authors.initials: ME
authors.lastname: Lockhart
authors.forename: Michelle L
authors.initials: ML
authors.lastname: Robbin
authors.forename: Kurt R
authors.initials: KR
authors.lastname: Zinn
mesh_terms: 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
chemical_list: D000911:Antibodies, Monoclonal; D061067:Antibodies, Monoclonal, Humanized; D003287:Contrast Media; D005466:Fluorocarbons; D000068258:Bevacizumab; C042852:perflutren