Litcius/Paper detail

Can contrast-enhanced mammography replace dynamic contrast-enhanced MRI in the assessment of sonomammographic indeterminate breast lesions?

Rasha Kamal, Mennatallah Mohamed Hanafy, Sahar Mansour, Maher Hassan, Mahmoud Gomaa

2020The Egyptian Journal of Radiology and Nuclear Medicine13 citationsDOIOpen Access PDF

Abstract

Abstract 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 contrast-enhanced (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.

Topics & Concepts

MedicineMammographyDynamic contrastIndeterminateContrast (vision)Breast MRIRadiologyDynamic contrast-enhanced MRIMagnetic resonance imagingBreast imagingNuclear medicineBreast cancerInternal medicineComputer scienceCancerArtificial intelligencePure mathematicsMathematicsMRI in cancer diagnosisAdvanced MRI Techniques and ApplicationsRadiomics and Machine Learning in Medical Imaging