Comparative study between conventional ultrasound strain elastography and an AI-enabled elastography software in differentiating breast masses

Background: Conventional ultrasound elastography is a relatively novel noninvasive imaging study that assesses tissue stiffness and helps in the characterization of breast lesions. However, strain elastography is not available in some ultrasound machines especially those before 2003 and is susceptible by motion artifacts. Our aim was to compare the results of conventional ultrasound elastography and the results of an advanced intelligence-enabled elastography software. Also, we aimed to assess the feasibility of the AI-enabled elastography software to overcome the unavailability of the conventional elastography software in some new ultrasound machines. Results: The study included 53 patients, who had breast lesions either clinically felt or detected during screening. All patients were subjected to both grayscale US imaging and conventional ultrasound elastography; quasi-static compression was applied during acquiring one of the cine-loops of the grayscale US imaging. Also, the cine-loops of the grayscale US imaging while quasi-static compression were processed by an AI-enabled elastography software. Then, the results of the strain ratio (SR) calculated by conventional elastography software and those by AI-enabled elastography software were compared. The strain ratio calculated using the AI-enabled elastography software showed better results than conventional ultrasound elastography strain ratio. The AI-enabled software shows better specificity, sensitivity, positive predictive values, and negative predictive values than the conventional ultrasound elastography. Conclusion: The AI-enabled elastography software shows promising results compared to the conventional US elastography. Elastography does not have the potential to replace conventional B-mode US for the detection of breast cancer but may complement the conventional US to improve diagnostic performance.