Faculty Bibliography

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.

Breast density refers to the amount of fibroglandular tissue relative to fat on mammography and is determined either qualitatively through visual assessment or quantitatively. It is a heritable and dynamic trait associated with age, race/ethnicity, body mass index, and hormonal factors. Increased breast density has important clinical implications including the potential to mask malignancy and as an independent risk factor for the development of breast cancer. Breast density has been incorporated into breast cancer risk models. Given the impact of dense breasts on the interpretation of mammography, supplemental screening may be indicated.

3D imaging enables accurate diagnosis by providing spatial information about organ anatomy. However, using 3D images to train AI models is computationally challenging because they consist of 10x or 100x more pixels than their 2D counterparts. To be trained with high-resolution 3D images, convolutional neural networks resort to downsampling them or projecting them to 2D. We propose an effective alternative, a neural network that enables efficient classification of full-resolution 3D medical images. Compared to off-the-shelf convolutional neural networks, our network, 3D Globally-Aware Multiple Instance Classifier (3D-GMIC), uses 77.98%-90.05% less GPU memory and 91.23%-96.02% less computation. While it is trained only with image-level labels, without segmentation labels, it explains its predictions by providing pixel-level saliency maps. On a dataset collected at NYU Langone Health, including 85,526 patients with full-field 2D mammography (FFDM), synthetic 2D mammography, and 3D mammography, 3D-GMIC achieves an AUC of 0.831 (95% CI: 0.769-0.887) in classifying breasts with malignant findings using 3D mammography. This is comparable to the performance of GMIC on FFDM (0.816, 95% CI: 0.737-0.878) and synthetic 2D (0.826, 95% CI: 0.754-0.884), which demonstrates that 3D-GMIC successfully classified large 3D images despite focusing computation on a smaller percentage of its input compared to GMIC. Therefore, 3D-GMIC identifies and utilizes extremely small regions of interest from 3D images consisting of hundreds of millions of pixels, dramatically reducing associated computational challenges. 3D-GMIC generalizes well to BCS-DBT, an external dataset from Duke University Hospital, achieving an AUC of 0.848 (95% CI: 0.798-0.896).

Breast MRI has high sensitivity and negative predictive value, making it well suited to problem solving when other imaging modalities or physical examinations yield results that are inconclusive for the presence of breast cancer. Indications for problem-solving MRI include equivocal or uncertain imaging findings at mammography and/or US; suspicious nipple discharge or skin changes suspected to represent an abnormality when conventional imaging results are negative for cancer; lesions categorized as Breast Imaging Reporting and Data System 4, which are not amenable to biopsy; and discordant radiologic-pathologic findings after biopsy. MRI should not precede or replace careful diagnostic workup with mammography and US and should not be used when a biopsy can be safely performed. The role of MRI in characterizing calcifications is controversial, and management of calcifications should depend on their mammographic appearance because ductal carcinoma in situ may not appear enhancing on MR images. In addition, ductal carcinoma in situ detected solely with MRI is not associated with a higher likelihood of an upgrade to invasive cancer compared with ductal carcinoma in situ detected with other modalities. MRI for triage of high-risk lesions is a subject of ongoing investigation, with a possible future role for MRI in decreasing excisional biopsies. The accuracy of MRI is likely to increase with the use of advanced techniques such as deep learning, which will likely expand the indications for problem-solving MRI. ^©RSNA, 2023 Quiz questions for this article are available in the supplemental material.

A 50-year-old woman with persistent axillary lymphadenopathy 17 weeks following COVID-19 vaccination was ultimately diagnosed with biopsy-proven benign reactive lymphadenopathy. In contrast, a 60-year-old woman with axillary lymphadenopathy and concurrent suspicious breast findings 9 weeks following COVID-19 vaccination was ultimately diagnosed with biopsy-proven metastatic breast carcinoma. This article reviews the current guidelines regarding breast cancer screening and management of axillary lymphadenopathy in the setting of COVID-19 vaccination.

Deep neural networks (DNNs) show promise in image-based medical diagnosis, but cannot be fully trusted since they can fail for reasons unrelated to underlying pathology. Humans are less likely to make such superficial mistakes, since they use features that are grounded on medical science. It is therefore important to know whether DNNs use different features than humans. Towards this end, we propose a framework for comparing human and machine perception in medical diagnosis. We frame the comparison in terms of perturbation robustness, and mitigate Simpson's paradox by performing a subgroup analysis. The framework is demonstrated with a case study in breast cancer screening, where we separately analyze microcalcifications and soft tissue lesions. While it is inconclusive whether humans and DNNs use different features to detect microcalcifications, we find that for soft tissue lesions, DNNs rely on high frequency components ignored by radiologists. Moreover, these features are located outside of the region of the images found most suspicious by radiologists. This difference between humans and machines was only visible through subgroup analysis, which highlights the importance of incorporating medical domain knowledge into the comparison.