Brief communication: The current status of contrast-enhanced mammography in breast imaging.

Improvising and developing state of the art techniques for breast cancer detection have always been an area of great interest in the field of imaging. Adding intravenous contrast to any imaging study, is well-known to increase the sensitivity and specificity of detection of a pathological process, especially in the setting of neoplasia secondary to tumor neoangiogenesis. Contrast enhanced MRI is known to be highly sensitive breast cancer screening tool till date, however, has been limited by long scan times, claustrophobia experienced by some women and high false positive findings. Despite continued advances in digital mammography technique, significant limitations have always been experienced in detection of small cancers especially in the setting of dense breast parenchyma. Implementing dual energy subtraction technique to digital mammography, made contrast enhanced mammography a viable technique to improve cancer detection. We aim to discuss the status of contrast enhanced mammography in this brief communication, emphasizing technical background, image acquisition, clinical applications, and future directions.

Artifacts in contrast-enhanced mammography: are there differences between vendors?

PURPOSE: Contrast-Enhanced Mammography (CEM) produces a dual-energy subtracted (DES) image that demonstrates iodine uptake (neovascularity) in breast tissue. We aim to review a range of artifacts on DES images produced using equipment from two different vendors and compare their incidence and subjective severity. METHODS: We retrospectively reviewed CEM studies performed between September 2013 and March 2017 using GE Senographe Essential (n = 100) and Hologic Selenia Dimensions (n = 100) equipment. Artifacts were categorized and graded in severity by a subspecialist breast radiologist and one of two medical imaging technologists in consensus. The incidence of artifacts between vendors was compared by calculating the relative risk, and the severity gradings were compared using a Wilcoxon rank-sum test. RESULTS: Elephant rind, corrugations and the black line on chest wall artifact were seen exclusively in Hologic images. Artifacts such as cloudy fat, negative rim around lesion and white line on pectoral muscle were seen in significantly more Hologic images (p < 0.05) whilst halo, ripple, skin line enhancement, black line on pectoral muscle, bright pectorals, chest wall high-lighting and air gap were seen in significantly more GE images (p < 0.05). The severity gradings for cloudy fat had a significantly higher mean rank in Hologic images (p < 0.001) whilst halo and ripple artifacts had a significantly higher mean rank in GE images (p < 0.001 and p = 0.028 respectively). CONCLUSION: The type, incidence and subjective severity of CEM-specific artifacts differ between vendors. Further research is needed, but differences in algorithms used to produce the DE image are postulated to be a significant contributor.

Artefacts in contrast enhanced digital mammography: how can they affect diagnostic image quality and confuse clinical diagnosis?

Contrast-enhanced digital mammography (CEDM) is a diagnostic tool for breast cancer detection. Artefacts are observed in about 10% of CEDM examinations. Understanding CEDM artefacts is important to prevent diagnostic misinterpretation. In this article, we have described the artefacts that we have commonly encountered in clinical practice; we hope to ease the recognition and help troubleshoot solutions to prevent or minimise them.

Classification of Breast Masses Using a Computer-Aided Diagnosis Scheme of Contrast Enhanced Digital Mammograms.

Contrast-enhanced digital mammography (CEDM) is a promising imaging modality in breast cancer diagnosis. This study aims to investigate how to optimally develop a computer-aided diagnosis (CAD) scheme of CEDM images to classify breast masses. A CEDM dataset of 111 patients was assembled, which includes 33 benign and 78 malignant cases. Each CEDM includes two types of images namely, low energy (LE) and dual-energy subtracted (DES) images. A CAD scheme was applied to segment mass regions depicting on LE and DES images separately. Optimal segmentation results generated from DES images were also mapped to LE images or vice versa. After computing image features, multilayer perceptron based machine learning classifiers that integrate with a correlation-based feature subset evaluator and leave-one-case-out cross-validation method were built to classify mass regions. When applying CAD to DES and LE images with original segmentation, areas under ROC curves (AUC) were 0.759 ± 0.053 and 0.753 ± 0.047, respectively. After mapping the mass regions optimally segmented on DES images to LE images, AUC significantly increased to 0.848 ± 0.038 (p < 0.01). Study demonstrated that DES images eliminated overlapping effect of dense breast tissue, which helps improve mass segmentation accuracy. The study demonstrated that applying a novel approach to optimally map mass region segmented from DES images to LE images enabled CAD to yield significantly improved performance.