Accumulating evidence shows that contrast-enhanced mammography (CEM) has higher diagnostic performance than digital mammography and ultrasound and comparable diagnostic performance to MRI for various indications. CEM also offers certain practical advantages for patients. Nevertheless, the clinical implementation of CEM has been limited because of a range of factors. This AJR Expert Panel Narrative Review explores such factors hindering CEM implementation. These factors include the following: the risks of iodinated contrast media, increased radiation exposure, indications for which CEM is not the preferred test or for which further evidence is needed, workflow adjustments needed when performing CEM examinations, incomplete availability of CEM-guided biopsy systems, and reimbursement challenges. Considerations that currently mitigate or are expected to mitigate these factors are also highlighted. 

Barriers to Implementation of Contrast-Enhanced Mammography in Clinical Practice: <i>AJR</i> Expert Panel Narrative Review

• The use of CEM-guided biopsy is spreading after initial experiences in a few pilot centers. • CEM-guided biopsy has the potential to ensure fast, low-cost, and effective tissue sampling of MRI-detected and CEM-detected lesions that do not have a corresponding finding at morphological imaging. • The need and utility of CEM-guided biopsy are warranted by the impending expansion of morpho-functional imaging towards breast cancer screening for women with extremely dense breasts. 

/pdf/contrast-enhanced-mammography-guided-biopsy-why-when-and-26qfttfw.pdf

Contrast-enhanced mammography-guided biopsy: why, when, and where we need it

Accumulating evidence shows that contrast-enhanced mammography (CEM) has higher diagnostic performance than digital mammography and ultrasound, and comparable diagnostic performance as MRI, for various indications. CEM also offers certain practical advantages for patients. Nevertheless, the clinical implementation of CEM has been limited due to a range of factors. This AJR Expert Panel Narrative Review explores such factors hindering CEM implementation. These include: risks of iodinated contrast media, increased radiation exposure, indications for which CEM is not the preferred test or for which further evidence is needed, workflow adjustments when performing CEM examinations, incomplete availability of CEM-guided biopsy systems, and reimbursement challenges. Considerations that currently or are expected to mitigate these factors are also highlighted.

Barriers to Implementation of Contrast-Enhanced Mammography in Clinical Practice: AJR Expert Panel Narrative Review.

Simple Summary Screening mammography reduces mortality from breast malignancy. However, breast cancer screening is, unfortunately, hindered due to the poor sensitivity of mammography in dense breasts: up to 15–30% of all cancers may be missed. Given the rapid development of Contrast-Enhanced Mammography (CEM) and its potential for diagnostic use, even in an asymptomatic population, it seems very important to correctly assess the Average Glandular Dose (AGD) for a single CEM examination. Few studies have compared the AGD of CEM versus Digital Mammography (DM) and protocols, including Digital Breast Tomosynthesis (DBT) plus DM, in the same group of patients. The additional role of tomosynthesis versus digital mammography in asymptomatic patients with dense breasts in screening examinations has been well investigated with encouraging results. In this study, we intend to compare the AGD and the diagnostic performance of CEM versus DM, and of CEM versus DM + DBT, performed in the same group of patients over the same period of time in a screening setting. Abstract This study aims to evaluate the Average Glandular Dose (AGD) and diagnostic performance of CEM versus Digital Mammography (DM) as well as versus DM plus one-view Digital Breast Tomosynthesis (DBT), which were performed in the same patients at short intervals of time. A preventive screening examination in high-risk asymptomatic patients between 2020 and 2022 was performed with two-view Digital Mammography (DM) projections (Cranio Caudal and Medio Lateral) plus one Digital Breast Tomosynthesis (DBT) projection (mediolateral oblique, MLO) in a single session examination. For all patients in whom we found a suspicious lesion by using DM + DBT, we performed (within two weeks) a CEM examination. AGD and compression force were compared between the diagnostic methods. All lesions identified by DM + DBT were biopsied; then, we assessed whether lesions found by DBT were also highlighted by DM alone and/or by CEM. We enrolled 49 patients with 49 lesions in the study. The median AGD was lower for DM alone than for CEM (3.41 mGy vs. 4.24 mGy, p = 0.015). The AGD for CEM was significantly lower than for the DM plus one single projection DBT protocol (4.24 mGy vs. 5.55 mGy, p < 0.001). We did not find a statistically significant difference in the median compression force between the CEM and DM + DBT. DM + DBT allows the identification of one more invasive neoplasm one in situ lesion and two high-risk lesions, compared to DM alone. The CEM, compared to DM + DBT, failed to identify only one of the high-risk lesions. According to these results, CEM could be used in the screening of asymptomatic high-risk patients.

/pdf/breast-digital-tomosynthesis-versus-contrast-enhanced-2dkcsm3d.pdf

Breast Digital Tomosynthesis versus Contrast-Enhanced Mammography: Comparison of Diagnostic Application and Radiation Dose in a Screening Setting

Contrast-enhanced mammography for screening recalls: a problem-solving assessment tool ready for use?

To evaluate the potential of contrast-enhanced mammography (CEM) for reducing the biopsy rate of screening recalls.Recalled women were prospectively enrolled to undergo CEM alongside standard assessment (SA) through additional views, tomosynthesis, and/or ultrasound. Exclusion criteria were symptoms, implants, allergy to contrast agents, renal failure, and pregnancy. SA and CEM were independently evaluated by one of six radiologists, who recommended biopsy or 2-year follow-up. Biopsy rates according to SA or recombined CEM (rCEM) were compared with the McNemar's test. Diagnostic performance was calculated considering lesions with available final histopathology.Between January 2019 and July 2021, 220 women were enrolled, 207 of them (median age 56.6 years) with 225 suspicious findings analysed. Three of 207 patients (1.4%) developed mild self-limiting adverse reactions to iodinated contrast agent. Overall, 135/225 findings were referred for biopsy, 90/225 by both SA and rCEM, 41/225 by SA alone and 4/225 by rCEM alone (2/4 being one DCIS and one invasive carcinoma). The rCEM biopsy rate (94/225, 41.8%, 95% CI 35.5-48.3%) was 16.4% lower (p < 0.001) than the SA biopsy rate (131/225, 58.2%, 95% CI 51.7-64.5%). Considering the 124/135 biopsies with final histopathology (44 benign, 80 malignant), rCEM showed a 93.8% sensitivity (95% CI 86.2-97.3%) and a 65.9% specificity (95% CI 51.1-78.1%), all 5 false negatives being ductal carcinoma in situ detectable as suspicious calcifications on low-energy images.Compared to SA, the rCEM-based work-up would have avoided biopsy for 37/225 (16.4%) suspicious findings. Including low-energy images in interpretation provided optimal overall CEM sensitivity.• The work-up of suspicious findings detected at mammographic breast cancer screening still leads to a high rate of unnecessary biopsies, involving between 2 and 6% of screened women. • In 207 recalled women with 225 suspicious findings, recombined images of contrast-enhanced mammography (CEM) showed a 93.8% sensitivity and a 65.9% specificity, all 5 false negatives being ductal carcinoma in situ detectable on low-energy images as suspicious calcifications. • CEM could represent an easily available one-stop shop option for the morphofunctional assessment of screening recalls, potentially reducing the biopsy rate by 16.4%. 

/pdf/contrast-enhanced-mammography-for-the-assessment-of-1wcswvbt.pdf

Contrast-enhanced mammography for the assessment of screening recalls: a two-centre study

Breast arterial calcifications (BAC) are a sex-specific cardiovascular disease biomarker that might improve cardiovascular risk stratification in women. We implemented a deep convolutional neural network for automatic BAC detection and quantification.In this retrospective study, four readers labelled four-view mammograms as BAC positive (BAC+) or BAC negative (BAC-) at image level. Starting from a pretrained VGG16 model, we trained a convolutional neural network to discriminate BAC+ and BAC- mammograms. Accuracy, F1 score, and area under the receiver operating characteristic curve (AUC-ROC) were used to assess the diagnostic performance. Predictions of calcified areas were generated using the generalized gradient-weighted class activation mapping (Grad-CAM++) method, and their correlation with manual measurement of BAC length in a subset of cases was assessed using Spearman ρ.A total 1493 women (198 BAC+) with a median age of 59 years (interquartile range 52-68) were included and partitioned in a training set of 410 cases (1640 views, 398 BAC+), validation set of 222 cases (888 views, 89 BAC+), and test set of 229 cases (916 views, 94 BAC+). The accuracy, F1 score, and AUC-ROC were 0.94, 0.86, and 0.98 in the training set; 0.96, 0.74, and 0.96 in the validation set; and 0.97, 0.80, and 0.95 in the test set, respectively. In 112 analyzed views, the Grad-CAM++ predictions displayed a strong correlation with BAC measured length (ρ = 0.88, p < 0.001).Our model showed promising performances in BAC detection and in quantification of BAC burden, showing a strong correlation with manual measurements.Integrating our model to clinical practice could improve BAC reporting without increasing clinical workload, facilitating large-scale studies on the impact of BAC as a biomarker of cardiovascular risk, raising awareness on women's cardiovascular health, and leveraging mammographic screening.• We implemented a deep convolutional neural network (CNN) for BAC detection and quantification. • Our CNN had an area under the receiving operator curve of 0.95 for BAC detection in the test set composed of 916 views, 94 of which were BAC+ . • Furthermore, our CNN showed a strong correlation with manual BAC measurements (ρ = 0.88) in a set of 112 views. 

/pdf/detection-and-quantification-of-breast-arterial-imc188w9.pdf

Diana Spinelli

Papers

Contrast-enhanced mammography for the assessment of screening recalls: a two-centre study

Detection and quantification of breast arterial calcifications on mammograms: a deep learning approach.