Background Dynamic contrast-enhanced MRI (DCE-MRI) is a revolution regarding screening and diagnosis of breast cancer. Yet, sometimes it is not the appropriate choice of imaging since the examination needs to be scheduled and may take place in another department. Contrast-enhanced mammography (CEM) is contrast-based digital mammogram, and consequently, it has emerged as a potential and promising replacer to DCE-MRI. Main body of the abstract There is a frequently asked question during the multidisciplinary breast cancer tumor boards is: which modality is more appropriate to be used in each clinical scenario? This article provided a detailed understanding of these two modalities in order to achieve a successful implementation of them into the clinical practice. Which modality to start with, in the context of the detection (screening) followed by characterization or diagnosis of the identified lesions? What is the appropriate application of both modalities in local staging and follow-up? All of these issues would be discussed in this article. Short conclusion MRI is a safe tool for breast imaging and has a superior diagnostic performance compared to CEM. However, CEM is getting close: this lies in its accessibility, short-time procedure, requirement of less training and feasibility to standardize.

Background: Dynamic contrast-enhanced MRI of the breast has been used for several years in the assessment of indeterminate mammographic findings. Contrast-enhanced mammography is a relatively novel imaging technique that has shown comparable sensitivity and specificity to MRI. Contrast-enhanced mammography is a relatively easy feasible study with high sensitivity and low cost. Our aim was to assess the feasibility of replacing dynamic contrastenhanced (DCE)-MRI by contrast-enhanced mammography in the assessment of sonomammographic indeterminate lesions (BIRADS 3 and 4). Results: The study included 82 patients with 171 breast lesions. They all performed contrast-enhanced mammography and dynamic contrast-enhanced MRI. DCE-MRI sensitivity and NPV were significantly higher than those of contrast-enhanced mammogram (CEM). The overall accuracy of MRI was better than that of CEM; however, no statistically significant difference could be detected. Conclusion: Contrast-enhanced mammography and dynamic contrast-enhanced MRI improved the characterization of breast lesions. CEM showed slightly lower sensitivity and accuracy compared to MRI; however, because of being relatively easy, available, cheap, and acceptable by women, CEM can replace DC-MRI as a problem-solving tool in the characterization of indeterminate breast lesions.

Background: Mammography is accused of having low sensitivity and specificity in dense breast parenchyma. Also, women with dense breasts show an increased risk of developing breast cancer. Breast ultrasound has been used for several years for a better characterization of breast lesions. Contrast-enhanced mammography and tomosynthesis are relative novel imaging techniques that have been implicated in breast cancer detection and diagnosis. We aimed to compare breast tomosynthesis, contrast-enhanced mammography, and breast ultrasound as complementary techniques to mammography in dense breast parenchyma. Results: The study included 37 patients with 63 inconclusive mammography breast lesions. They all performed contrast-enhanced mammography, single-view tomosynthesis, and breast ultrasound. Mammography had a sensitivity of 83%, a specificity of 48%, a positive predictive value of 68%, a negative predictive value of 68%, and a diagnostic accuracy of 68%. Contrast-enhanced mammography had a sensitivity of 89%, a specificity of 89%, a positive predictive value of 91%, a negative predictive value of 86%, and a diagnostic accuracy of 89%. Tomosynthesis had a sensitivity of 86%, a specificity of 81%, a positive predictive value of 86%, a negative predictive value of 81%, and a diagnostic accuracy of 84%. Breast ultrasound had a sensitivity of 97%, a specificity of 85%, a positive predictive value of 90%, a negative predictive value of 96%, and a diagnostic accuracy of 92%. Conclusion: Breast ultrasound, tomosynthesis, and contrast-enhanced mammography showed better performance compared to mammography in dense breasts. However, ultrasound being safe with no radiation hazards should be the second step modality of choice after mammography in the assessment of mammography dense breasts. Adding tomosynthesis to mammography in screening increases its sensitivity. Contrast-enhanced mammography should be reserved for cases with inconclusive sonomammographic results.

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.

No abstract