Characterization of lesions in ultrasound and elastography images using machine learning methods

Many procedures have been developed to aid in the early detection and diagnosis of breast cancer. In this context, Computer-Aided Diagnosis (CADx) systems were designed to provide to the specialist a reliable second opinion. This study presents the proposal of investigating the diagnostic ability of a computational system in the characterization of suspicious findings in B-mode ultrasound and breast elastography imaging. The database consisted of 31 malignant and 52 benign lesions and an additional data set containing 206 lesions (144 benign and 62 malignant) seen only on the B-mode ultrasound for performing the machine learning tests. Three radiologists drew manually the contour of the lesions in B-mode ultrasound and we used an automatic technique to segment the lesions. Then, the contour was mapped in the elastography image. The lesions were classified using the CADx system developed for B-mode ultrasound and strain elastography. We calculated the sensitivity, specificity and AUC to evaluate the data. The developed CADx system provided a diagnostic concordance in the classification of breast lesions from the different ways of contour determination (manual and automatic), allowing to reduce the diagnostic variability. In addition, the CADx system showed superior results to the visual analysis of the radiologist. When the radiologist associated both examinations (B-mode ultrasound and elastography), his visual analysis provided 87.10%, 55.77% and 0.714 of sensitivity, specificity and AUC, respectively. When we considered the result provided by the association between B-mode ultrasound and elastography images, the CADx system provided a comparative increase of about 7% of sensitivity and 17.2% of specificity, using the contour delimited by the most experienced radiologist. In addition, a positive influence was observed in the use of the computational tool by radiologists, since, on average, their sensitivity and specificity indexes also increased in relation to the conventional analysis, from 87.1% and 55.8% to 90.3% and 73.1%, respectively. (AU)