Breast cancer screening needs assessment in 19 Northern California counties: geography, poverty, and racial/ethnic identity composition.

Purpose: To describe the area-level rate of breast cancers, the percentage of early-stage diagnoses (stage I-IIa), and associations between area-level measures of poverty, racial/ethnic composition, primary care shortage, and urban/rural/frontier status for the UC Davis Comprehensive Cancer Center (UCDCCC) catchment area.

Methods: Using data from the SEER Cancer Registry of Greater California (2014-2018) and the California Department of Health Care Access and Information Medical Service Study Area, we conducted an ecological study in the UCDCCC catchment area to identify geographies that need screening interventions and their demographic characteristics.

Results: The higher the percentage of the population identifying as Hispanic/Latino/Latinx, and the higher the percentage of the population below the 100% poverty level, the lower the odds of being diagnosed at an early-stage (OR = 0.

Supplemental breast cancer screening after negative mammography in U.S. women with dense breasts.

The extent and determinants of supplemental screening among women with dense breasts are unclear. We evaluated a retrospective cohort of 498,855 women aged 40-74 years with heterogeneously or extremely dense breasts who obtained 1,176,251 negative screening mammography examinations during 2011-2019 in the United States. Overall, 2.

Medical imaging utilization in migrants compared with nonmigrants in a universal healthcare system: A population-based matched cohort study.

Background: Medical imaging is an integral part of healthcare. Globalization has resulted in increased mobilization of migrants to new host nations. The association between migration status and utilization of medical imaging is unknown.

Artificial Intelligence Algorithm for Subclinical Breast Cancer Detection.

Importance: Early breast cancer detection is associated with lower morbidity and mortality.

Objective: To examine whether a commercial artificial intelligence (AI) algorithm for breast cancer detection could estimate the development of future cancer.

Design, Setting, And Participants: This retrospective cohort study of 116 495 women aged 50 to 69 years with no prior history of breast cancer before they underwent at least 3 consecutive biennial screening examinations used scores from an AI algorithm (INSIGHT MMG, version 1.

Association Between False-Positive Results and Return to Screening Mammography in the Breast Cancer Surveillance Consortium Cohort.

Background: False-positive results on screening mammography may affect women's willingness to return for future screening.

Objective: To evaluate the association between screening mammography results and the probability of subsequent screening.

Design: Cohort study.