Faculty Bibliography

Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications of machine learning to breast MRI continue to expand rapidly as increasingly accurate 3D breast and lesion segmentation allows the combination of radiologist-level interpretation (eg, BI-RADS lexicon), data from advanced multiparametric imaging techniques, and patient-level data such as genetic risk markers. Advances in breast MRI feature extraction have led to rapid dataset analysis, which offers promise in large pooled multiinstitutional data analysis. The object of this review is to provide an overview of machine-learning and deep-learning techniques for breast MRI, including supervised and unsupervised methods, anatomic breast segmentation, and lesion segmentation. Finally, it explores the role of machine learning, current limitations, and future applications to texture analysis, radiomics, and radiogenomics. Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019.

The degree of normal fibroglandular tissue that enhances on breast MRI, known as background parenchymal enhancement (BPE), was initially described as an incidental finding that could affect interpretation performance. While BPE is now established to be a physiologic phenomenon that is affected by both endogenous and exogenous hormone levels, evidence supporting the notion that BPE frequently masks breast cancers is limited. However, compelling data have emerged to suggest BPE is an independent marker of breast cancer risk and breast cancer treatment outcomes. Specifically, multiple studies have shown that elevated BPE levels, measured qualitatively or quantitatively, are associated with a greater risk of developing breast cancer. Evidence also suggests that BPE could be a predictor of neoadjuvant breast cancer treatment response and overall breast cancer treatment outcomes. These discoveries come at a time when breast cancer screening and treatment have moved toward an increased emphasis on targeted and individualized approaches, of which the identification of imaging features that can predict cancer diagnosis and treatment response is an increasingly recognized component. Historically, researchers have primarily studied quantitative tumor imaging features in pursuit of clinically useful biomarkers. However, the need to segment less well-defined areas of normal tissue for quantitative BPE measurements presents its own unique challenges. Furthermore, there is no consensus on the optimal timing on dynamic contrast-enhanced MRI for BPE quantitation. This article comprehensively reviews BPE with a particular focus on its potential to increase precision approaches to breast cancer risk assessment, diagnosis, and treatment. It also describes areas of needed future research, such as the applicability of BPE to women at average risk, the biological underpinnings of BPE, and the standardization of BPE characterization. Level of Evidence: 3 Technical Efficacy Stage: 5.

Deep learning models designed for visual classification tasks on natural images have become prevalent in medical image analysis. However, medical images differ from typical natural images in many ways, such as significantly higher resolutions and smaller regions of interest. Moreover, both the global structure and local details play important roles in medical image analysis tasks. To address these unique properties of medical images, we propose a neural network that is able to classify breast cancer lesions utilizing information from both a global saliency map and multiple local patches. The proposed model outperforms the ResNet-based baseline and achieves radiologist-level performance in the interpretation of screening mammography. Although our model is trained only with image-level labels, it is able to generate pixel-level saliency maps that provide localization of possible malignant findings.

OBJECTIVE. The purpose of this study is to describe screening updates for women with average and high risk for breast cancer, compare different screening strategies, and describe new approaches in risk prediction, including radiomics. CONCLUSION. All women are at substantial risk for breast cancer. For women with average risk, annual mammography beginning at 40 years old maximizes the life-extending benefits and provides improved treatment options. Women at higher risk need earlier and more intense screening. Delaying initiation or decreasing frequency of mammographic screening adversely affects breast cancer detection.

As the proportion of women diagnosed with early stage breast cancer increases, the role of imaging for staging and surveillance purposes is considered. National and international guidelines discourage the use of staging imaging for asymptomatic patients newly diagnosed with stage 0 to II breast cancer, even if there is nodal involvement, as unnecessary imaging can delay care and affect outcomes. In asymptomatic patients with a history of stage I breast cancer that received treatment for curative intent, there is no role for imaging to screen for distant recurrences. However, routine surveillance with an annual mammogram is the only imaging test that should be performed to detect an in-breast recurrence or a new primary breast cancer in women with a history of stage I breast cancer. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.

Although computer-aided diagnosis (CAD) is widely used in mammography, conventional CAD programs that use prompts to indicate potential cancers on the mammograms have not led to an improvement in diagnostic accuracy. Because of the advances in machine learning, especially with use of deep (multilayered) convolutional neural networks, artificial intelligence has undergone a transformation that has improved the quality of the predictions of the models. Recently, such deep learning algorithms have been applied to mammography and digital breast tomosynthesis (DBT). In this review, the authors explain how deep learning works in the context of mammography and DBT and define the important technical challenges. Subsequently, they discuss the current status and future perspectives of artificial intelligence-based clinical applications for mammography, DBT, and radiomics. Available algorithms are advanced and approach the performance of radiologists-especially for cancer detection and risk prediction at mammography. However, clinical validation is largely lacking, and it is not clear how the power of deep learning should be used to optimize practice. Further development of deep learning models is necessary for DBT, and this requires collection of larger databases. It is expected that deep learning will eventually have an important role in DBT, including the generation of synthetic images.

Background Male breast cancer incidence is rising. There may be a potential role in selective screening in men at elevated risk for breast cancer, but the effectiveness of such screening remains unexplored. Purpose To evaluate patterns of male breast imaging utilization, to determine high-risk screening outcomes, and to delineate risk factors associated with cancer diagnosis. Materials and Methods This retrospective study reviewed consecutive male breast imaging examinations over a 12-year period (between 2005-2017). Examination indications, biopsy recommendations, and pathologic results were correlated with patient characteristics. Fisher exact test, Mann-Whitney test, Spearman correlation, and logistic regression were used for statistical analysis. Results A total of 1869 men (median age, 55 years; range, 18-96 years) underwent 2052 examinations yielding 2304 breast lesions and resulting in 149 (6.5%) biopsies in 133 men; 41 (27.5%) were malignant and 108 (72.5%) were benign. There were 1781 (86.8%) diagnostic and 271 (13.2%) screening examinations. All men undergoing screening had personal or family history of breast cancer and/or genetic mutations. There was a significant increase in the number of examinations in men relative to the number of examinations in women over time (Spearman correlation, r = 0.85; P < .001). Five node-negative cancers resulted from screening mammography, yielding a cancer detection rate of 18 per 1000 examinations (95% confidence interval [CI]: 7, 41), with cancers diagnosed on average after 4 person-years of screening (range, 1-10 person-years). Mammographic screening sensitivity, specificity, and positive predictive value of biopsy were 100% (95% CI: 50%, 100%), 95.0% (95% CI: 93.1%, 98%), and 50% (95% CI: 22.2%, 77.8%). Older age (P < .001), Ashkenazi descent (P < .001), genetic mutations (P = .006), personal history (P < .001), and first-degree family history (P = .03) were associated with breast cancer. Non-first-degree family history was not associated with cancer (P = .09). Conclusion There is potential benefit in screening men at high risk for developing breast cancer. Such screening may have increased over time. © RSNA, 2019.

Background Although previous studies have focused on rural disparities in the use of screening mammography, city-level use throughout the United States has not been well evaluated even though more than 30 million women live in the 500 largest cities. Purpose To evaluate disparities in the city-level use of screening mammography and to identify factors that have an impact on screening utilization. Materials and Methods This retrospective study used data from large publicly available databases, the American Community Survey and Robert Wood Johnson Foundation 500 Cities Project, which includes screening mammography utilization data from the Behavioral Risk Factor Surveillance System. Databases were searched from January to March 2018. The use of screening mammography was evaluated at the city level by census region and division by using the Mann-Whitney U test. Univariable Spearman rank correlation and multivariable regression analysis were performed to determine the impact of factors on screening use, including population size, health-related variables (use of Papanicolaou test, obesity), income variables (median household income, poverty status, health insurance), and race. Results Overall mean city-level screening mammography use rate was 77.7% (range, 62.8%-88.9%). The highest mean utilization occurred in coastal cities, with the highest overall utilization in the New England area (82.7%). The lowest utilization rate was in Mountain states (73.6%). City-level utilization showed a positive correlation with Papanicolaou test use (r = 0.75, P < .001), median household income (r = 0.44, P < .001), and percentage Asian population (r = 0.38, P < .001) and a negative correlation with obesity (r = -0.36, P < .001), the lack of health insurance (r = -0.44, P < .001), and poverty (r = -0.30, P < .001). Multivariable analysis showed the strongest independent predictors of utilization to be percentage of women screened with the Papanicolaou test, Asian race, private insurance, and census division (R² = 68%). Conclusion Disparities in the utilization of preventive health care services exist at the large city level, with the highest use in New England cities and lowest in Mountain cities. Predictors of higher than average utilization include census division and percentage of inhabitants who are up to date with the Papanicolaou test, are of Asian race, and have private insurance. © RSNA, 2019.

MRI of the breast has the highest sensitivity for breast cancer detection among current clinical imaging modalities and is indispensable for breast imaging practice. While the basis of breast MRI consists of T1-weighted contrast-enhanced imaging, T2-weighted, ultrafast, and diffusion-weighted imaging may be used to improve lesion characterization. Such multiparametric assessment of breast lesions allows for excellent discrimination between benign and malignant breast lesions. Indications for breast MRI are expanding. In preoperative staging, multiple studies confirm the superiority of MRI to other imaging modalities for tumor size estimation and detection of additional tumor foci in the ipsilateral and contralateral breast. Ongoing studies show that in experienced hands this can be used to improve breast cancer surgery, although there is no evidence of improved long-term outcomes. Screening indications are likewise growing as evidence is accumulating that OncologicRI depicts cancers at an earlier stage than mammography in all women. To manage the associated costs for screening, the use of abbreviated protocols may be beneficial. In patients treated with neoadjuvant chemotherapy, MRI is used to document response. It is essential to realize that oncologic and surgical response are different, and evaluation should be adapted to the underlying question.