Quantification of Acoustic Radiation Force Impulse in Differentiating Between Malignant and Benign Breast Lesions

Abstract

The aim of this study was to evaluate the use of gray-level quantification (GLQ) in virtual touch tissue imaging (VTI) in the differential diagnosis of breast lesions. GLQ values of 153 lesions (101 benign, 52 malignant) were analyzed with matrix laboratory software (MATLAB, The MathWorks, Natick, MA, USA), with gray levels ranging from 0 (pure black) to 255 (pure white). The diagnostic performance of GLQ was also evaluated using receiver operating characteristic curve analysis. The mean GLQ value for benign lesions (103.27 ± 39.44) differed significantly from that for malignant lesions (44.57 ± 13.61) (p < 0.001). At a cutoff value of 52.31, the sensitivity, specificity, accuracy, positive predictive value and negative predictive value were 86.5%, 93.1%, 90.8%, 86.5% and 93.1%, respectively. In conclusion, we have proposed a method for quantification of gray levels in VTI for the differential diagnosis of breast lesions. Our results indicate that this method has the potential to aid in the classification of benign and malignant breast masses.