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ARFI Variance of Acceleration for Diagnostic Breast Cancer Imaging in Women, in vivo

Early detection of breast cancer greatly aids survival. However, the specificity of current screening methods for identifying malignancy is poor, requiring costly and invasive additional tests and causing anxiety for the patient. Although some ultrasound methods have used mechanical properties to discriminate benign and malignant lesions, they are complicated by tissue features like fluid and necrosis. We propose a new metric, ΔLog(VoA), which can be calculated from ARFI ultrasound data and incorporates the mechanical and acoustic properties of tissue into one parameter. ΔLog(VoA) is statistically significantly lower in fluid- and necrosis-containing masses than solid ones (Wilcoxin, p<0.006). ΔLog(VoA) does not significantly differ between malignant and benign masses. However, it is significantly lower in the surrounding tissue of malignant masses than in the surrounding tissue of benign ones (Wilcoxin, p<0.02). These results suggest that ΔLog(VoA) can differentiate clinically relevant lesion features such as fluid and necrosis, and detect tissue characteristics that coincide with malignancy.