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City Patterns of Screening Mammography Uptake and Disparity across the United States

Kim, Eric; Moy, Linda; Gao, Yiming; Hartwell, C Austen; Babb, James S; Heller, Samantha L
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.
PMID: 31429681
ISSN: 1527-1315
CID: 4046742

Preoperative Breast MRI Is Not a Significant Prognostic Factor of Recurrence-Free Survival and Overall Survival in Young Women [Comment]

Kim, Eric; Moy, Linda
PMID: 36975823
ISSN: 1527-1315
CID: 5502592

Breast Cancer Screening and Axillary Adenopathy in the Era of COVID-19 Vaccination

Wolfson, Stacey; Kim, Eric
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.
PMID: 36219117
ISSN: 1527-1315
CID: 5360912

Thank You from the Radiology In Training Editors to Their Mentors [Editorial]

Kim, Eric; Deng, Francis; Trofimova, Anna
PMID: 35852427
ISSN: 1527-1315
CID: 5278932

Differences between human and machine perception in medical diagnosis

Makino, Taro; Jastrzębski, Stanisław; Oleszkiewicz, Witold; Chacko, Celin; Ehrenpreis, Robin; Samreen, Naziya; Chhor, Chloe; Kim, Eric; Lee, Jiyon; Pysarenko, Kristine; Reig, Beatriu; Toth, Hildegard; Awal, Divya; Du, Linda; Kim, Alice; Park, James; Sodickson, Daniel K; Heacock, Laura; Moy, Linda; Cho, Kyunghyun; Geras, Krzysztof J
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.
PMID: 35477730
ISSN: 2045-2322
CID: 5205672

Follow-up of COVID-19 Vaccine-related Axillary Lymphadenopathy before 12 weeks is Unnecessary [Comment]

Moy, Linda; Kim, Eric
PMID: 35471114
ISSN: 1527-1315
CID: 5217362

Breast Inflammatory Change Is Transient Following COVID-19 Vaccination

Kim, Eric; Reig, Beatriu
PMID: 35289660
ISSN: 1527-1315
CID: 5220702

Axillary Adenopathy after COVID-19 Vaccine: No Reason to Delay Screening Mammogram

Wolfson, Stacey; Kim, Eric; Plaunova, Anastasia; Bukhman, Rita; Sarmiento, Ruth D; Samreen, Naziya; Awal, Divya; Sheth, Monica M; Toth, Hildegard B; Moy, Linda; Reig, Beatriu
PMID: 35133198
ISSN: 1527-1315
CID: 5156732

2021 Top Images in Radiology: Radiology In Training Editors' Choices [Editorial]

Deng, Francis; Kim, Eric; Trofimova, Anna V; Lee, Susanna I
PMID: 34846205
ISSN: 1527-1315
CID: 5065512

Radiology In Training: The Inaugural Year Amidst a Pandemic [Editorial]

Kim, Eric; Trofimova, Anna; Deng, Francis; Lee, Susanna I
PMID: 34313472
ISSN: 1527-1315
CID: 5043402