Faculty Bibliography

Digital breast tomosynthesis (DBT) is a pseudo 3D mammography imaging technique that has become widespread since gaining Food and Drug Administration approval in 2011. With this technology, a variable number of tomosynthesis projection images are obtained over an angular range between 15° and 50° for currently available clinical DBT systems. The angular range impacts various aspects of clinical imaging, such as radiation dose, scan time, and image quality, including visualization of calcifications, masses, and architectural distortion. This review presents an overview of the differences between narrow- and wide-angle DBT systems, with an emphasis on their applications in clinical practice. Comparison examples of patients imaged on both narrow- and wide-angle DBT systems illustrate these differences. Understanding the potential variable appearance of imaging findings with narrow- and wide-angle DBT systems is important for radiologists, particularly when comparison images have been obtained on a different DBT system. Furthermore, knowledge about the comparative strengths and limitations of DBT systems is needed for appropriate equipment selection.

Digital breast tomosynthesis (DBT) has been widely adopted in breast imaging in both screening and diagnostic settings. The benefits of DBT are well established. Compared with two-dimensional digital mammography (DM), DBT preferentially increases detection of invasive cancers without increased detection of in-situ cancers, maximizing identification of biologically significant disease, while mitigating overdiagnosis. The higher sensitivity of DBT for architectural distortion allows increased diagnosis of invasive cancers overall and particularly improves the visibility of invasive lobular cancers. Implementation of DBT has decreased the number of recalls for false-positive findings at screening, contributing to improved specificity at diagnostic evaluation. Integration of DBT in diagnostic examinations has also resulted in an increased percentage of biopsies with positive results, improving diagnostic confidence. Although individual DBT examinations have a longer interpretation time compared with that for DM, DBT has streamlined the diagnostic workflow and minimized the need for short-term follow-up examinations, redistributing much-needed time resources to screening. Yet DBT has limitations. Although improvements in cancer detection and recall rates are seen for patients in a large spectrum of age groups and breast density categories, these benefits are minimal in women with extremely dense breast tissue, and the extent of these benefits may vary by practice environment and by geographic location. Although DBT allows detection of more invasive cancers than does DM, its incremental yield is lower than that of US and MRI. Current understanding of the biologic profile of DBT-detected cancers is limited. Whether DBT improves breast cancer-specific mortality remains a key question that requires further investigation. ^©RSNA, 2021.

BACKGROUND:Emerging data suggest screening mammography may be effective in detecting breast cancer early in high-risk men. We evaluated current screening recommendations as a risk management strategy in men at elevated risk for breast cancer. PATIENTS AND METHODS/METHODS:This institutional review board-approved, Health Insurance Portability and Accountability Act (HIPAA)-compliant study reviewed consecutive men who underwent genetic counseling and multigene panel testing (MGPT) for breast cancer risk assessment at our institution between 2012 and 2018. Patient risk factors, test indications, and MGPT outcomes were recorded, then correlated with screening recommendations by either clinical breast examination or screening mammography. Recommendation consistency among practitioners was evaluated. Patient adherence to screening mammography (defined as undergoing screening mammography as recommended) was assessed. Statistical analysis was performed at the 2-sided 5% significance level. RESULTS:A total of 414 asymptomatic men underwent both genetic counseling and MGPT (mean age, 47 years; range, 18-91 years) for breast cancer risk assessment. Of this group, 18 (4.3%) of 414 had a personal history of breast cancer, and 159 (38.4%) of 414 had a family history of breast cancer before MGPT. Among 112 men with positive MGPT results, BRCA1/2 mutations were the most common (56.3%, 63/112). Most BRCA mutation carriers (80.9%, 51/63) were recommended clinical breast examination only. Only 5.9% (2/34) BRCA2 and 10.3% (3/29) BRCA1 carriers were recommended screening mammograms (7.9%, 5/63 of all BRCA carriers). Among men with a personal history of breast cancer, only 9 (50%) of 18 were recommended screening mammograms. Overall adherence to screening mammogram in men was 71.4% (10/14), which ultimately yielded two cancers. Breast cancer screening recommendations varied widely among practitioners, with some recommending clinical breast examination only, and others also recommending mammography. CONCLUSION/CONCLUSIONS:Men who are found to be at an elevated risk for breast cancer after undergoing genetic counseling and testing currently receive relatively inconsistent screening recommendations.

Magnetic Resonance (MR) imaging is the most sensitive modality for breast cancer detection but is currently limited to screening women at high risk due to limited specificity and test accessibility. However, specificity of MR imaging improves with successive rounds of screening, and abbreviated approaches have the potential to increase access and decrease cost. There is growing evidence to support supplemental MR imaging in moderate-risk women, and current guidelines continue to evolve. Functional imaging has the potential to maximize survival benefit of screening. Leveraging MR imaging as a possible primary screening tool is therefore also being investigated in average-risk women.

ABSTRACT/UNASSIGNED:This study aimed to evaluate the effect of an artificial intelligence (AI) support system on breast ultrasound diagnostic accuracy.In this Health Insurance Portability and Accountability Act-compliant, institutional review board-approved retrospective study, 200 lesions (155 benign, 45 malignant) were randomly selected from consecutive ultrasound-guided biopsies (June 2017-January 2019). Two readers, blinded to clinical history and pathology, evaluated lesions with and without an Food and Drug Administration-approved AI software. Lesion features, Breast Imaging Reporting and Data System (BI-RADS) rating (1-5), reader confidence level (1-5), and AI BI-RADS equivalent (1-5) were recorded. Statistical analysis was performed for diagnostic accuracy, negative predictive value, positive predictive value (PPV), sensitivity, and specificity of reader versus AI BI-RADS. Generalized estimating equation analysis was used for reader versus AI accuracy regarding lesion features and AI impact on low-confidence score lesions. Artificial intelligence effect on false-positive biopsy rate was determined. Statistical tests were conducted at a 2-sided 5% significance level.There was no significant difference in accuracy (73 vs 69.8%), negative predictive value (100% vs 98.5%), PPV (45.5 vs 42.4%), sensitivity (100% vs 96.7%), and specificity (65.2 vs 61.9; P = 0.118-0.409) for AI versus pooled reader assessment. Artificial intelligence was more accurate than readers for irregular shape (74.1% vs 57.4%, P = 0.002) and less accurate for round shape (26.5% vs 50.0%, P = 0.049). Artificial intelligence improved diagnostic accuracy for reader-rated low-confidence lesions with increased PPV (24.7% AI vs 19.3%, P = 0.004) and specificity (57.8% vs 44.6%, P = 0.008).Artificial intelligence decision support aid may help improve sonographic diagnostic accuracy, particularly in cases with low reader confidence, thereby decreasing false-positives.

Ultrasound-guided hookwire localization was initially introduced to facilitate the excision of nonpalpable breast lesions by guiding surgical exploration, thereby reducing operative time and morbidity. The same technique has since found utility in a range of other applications outside breast and can be useful within the musculoskeletal system. Despite this, there remains limited literature with respect to its technical aspects and practical utility. We describe our technique and a series of preoperative ultrasound-guided wire localizations in the musculoskeletal system to assist surgical excision of 4 soft tissue masses.

Abbreviated and ultrafast breast MRI are emerging techniques that are now entering clinical practice and reflect an increasing understanding of breast cancer heterogeneity. These techniques may represent potential answers to shortcomings of mammographic screening, providing an opportunity to curb interval cancers, maximize diagnostic accuracy, and minimize overdiagnosis. Targeting more aggressive tumor subtypes may play a role in evidence-based de-escalation of breast cancer management, and abbreviated techniques have proved promising in early noninferiority studies. Functional characterization of tumors at MRI also has the potential for noninvasive tumor subtyping based on radiomics and radiogenomics and may ultimately streamline increasingly individualized breast cancer care. The purpose of this article is to describe techniques of abbreviated and ultrafast breast MRI, recognize their pros and cons, and discuss clinical applications and implications. The goals are to define terminology, consider diagnostic parameters, and emphasize key concepts. As these novel techniques enter clinical care and continue to evolve, it is essential that the radiologist understands the rationale and limitations behind these approaches and how and why interpretation may differ from that of conventional MRI. ^©RSNA, 2020.

We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200,000 exams (over 1,000,000 images). Our network achieves an AUC of 0.895 in predicting the presence of cancer in the breast, when tested on the screening population. We attribute the high accuracy to a few technical advances. (i) Our network's novel two-stage architecture and training procedure, which allows us to use a high-capacity patch-level network to learn from pixel-level labels alongside a network learning from macroscopic breast-level labels. (ii) A custom ResNet-based network used as a building block of our model, whose balance of depth and width is optimized for high-resolution medical images. (iii) Pretraining the network on screening BI-RADS classification, a related task with more noisy labels. (iv) Combining multiple input views in an optimal way among a number of possible choices. To validate our model, we conducted a reader study with 14 readers, each reading 720 screening mammogram exams, and show that our model is as accurate as experienced radiologists when presented with the same data. We also show that a hybrid model, averaging the probability of malignancy predicted by a radiologist with a prediction of our neural network, is more accurate than either of the two separately. To further understand our results, we conduct a thorough analysis of our network's performance on different subpopulations of the screening population, the model's design, training procedure, errors, and properties of its internal representations. Our best models are publicly available at https://github.com/nyukat/breastcancerclassifier.