Digital breast tomosynthesis (DBT) is an emerging technology used in diagnostic breast imaging to evaluate potential abnormalities. In DBT, the compressed breast tissue is imaged in a quasi-three-dimensional manner by performing a series of low-dose radiographic exposures and using the resultant projection image dataset to reconstruct cross-sectional in-plane images in standard mammographic views. Improved visualization of breast detail at diagnostic DBT allows improved characterization of findings, including normal structures and breast cancer. This technology reduces the summation of overlapping breast tissue, which can mimic breast cancer, and provides improved detail of noncalcified mammographic findings seen in breast cancer. It also assists in lesion localization and determining mammographic extent of disease in women with known or suspected breast cancer. The authors review the potential uses, benefits, and limitations of DBT in the diagnostic setting and discuss how radiologists can best use DBT to characterize lesions, localize potential abnormalities, and evaluate the extent of known or suspected breast cancer. The authors' experience shows that DBT can be implemented effectively in the diagnostic workflow to evaluate and localize potential lesions more efficiently. DBT may potentially replace conventional supplemental mammography at diagnostic workup and obviate ultrasonography in select cases.
The aim was to determine whether an artificial intelligence (AI)-based, computer-aided detection (CAD) software can be used to reduce false positive per image (FPPI) on mammograms as compared to an FDA-approved conventional CAD. A retrospective study was performed on a set of 250 full-field digital mammograms between January 1, 2013, and March 31, 2013, and the number of marked regions of interest of two different systems was compared for sensitivity and specificity in cancer detection. The count of false-positive marks per image (FPPI) of the two systems was also evaluated as well as the number of cases that were completely mark-free. All results showed statistically significant reductions in false marks with the use of AI-CAD vs CAD (confidence interval = 95%) with no reduction in sensitivity. There is an overall 69% reduction in FPPI using the AI-based CAD as compared to CAD, consisting of 83% reduction in FPPI for calcifications and 56% reduction for masses. Almost half (48%) of cases showed no AI-CAD markings while only 17% show no conventional CAD marks. There was a significant reduction in FPPI with AI-CAD as compared to CAD for both masses and calcifications at all tissue densities. A 69% decrease in FPPI could result in a 17% decrease in radiologist reading time per case based on prior literature of CAD reading times. Additionally, decreasing false-positive recalls in screening mammography has many direct social and economic benefits.
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