Abstract
A statistical contrast-enhanced ultrasound (CEUS) method, which describes the change in the histogram of image intensity during microbubble wash-in, was developed previously in our lab as a means of characterizing the spatial heterogeneity of tumor perfusion. This study tests whether that statistical CEUS method should be preferred to conventional mean-intensity-based CEUS analysis for classification of anti-angiogenic treatment responses in a preclinical tumor model. Seven perfusion parameters from conventional and statistical CEUS were fed into logistic regression and support vector machine learning models to classify control (modeling resistant) and treated (modeling sensitive) tumors engrafted on a chicken embryo angiogenic assay. Learning models combining features from both conventional and statistical CEUS analysis more accurately classified anti-angiogenic response than models using either statistical or conventional features alone. Therefore, the statistical CEUS method is best used as a supplement to conventional CEUS analysis.
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