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Background: Breast cancer screening (BCS) inequities are evident at national and local levels, and many health systems want to address these inequities, but may lack data about contributing factors. The objective of this study was to inform health system interventions through an exploratory analysis of potential multilevel contributors to BCS inequities using health system data.

Methods: The authors conducted a cross-sectional analysis within a large academic health system including 19,774 individuals who identified as Black (n = 1445) or White (n = 18,329) race and were eligible for BCS.

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External validation of a multivariable prediction model for positive resection margins in breast-conserving surgery.

BMC Res Notes

January 2025

Department of Surgery, Department of Clinical Sciences, Division of Surgery, Skåne University Hospital, Lund University, Lund, Sweden.

Objectives: Positive resection margins after breast-conserving surgery (BCS) most often demands a repeat surgery. To preoperatively identify patients at risk of positive margins, a multivariable model has been developed that predicts positive margins after BCS with a high accuracy. This study aimed to externally validate this prediction model to explore its generalizability and assess if additional preoperatively available variables can further improve its predictive accuracy.

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[Analysis of Factors That Promote Awareness of Breast MRI Surveillance for Carriers of Hereditary Breast Cancer Risk Genes ( BRCA1/2)].

Nihon Hoshasen Gijutsu Gakkai Zasshi

January 2025

Department of Risk Analysis and Biodosimetry, Institute of Radiation Emergency Medicine, Hirosaki University.

Purpose: Hereditary breast and ovarian cancers (HBOC) carry a high risk of breast cancer, and detailed screening with contrast-enhanced breast MRI (breast MRI surveillance) is recommended. With the increase in the number of individuals diagnosed with HBOC, the demand for breast MRI surveillance is also rising. However, the current system is inadequate, with factors such as lack of knowledge and indifference among healthcare professionals, and insufficient understanding of breast MRI surveillance being cited.

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Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy.

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Background/objectives: Breast cancer is a significant global health concern, and early detection is crucial for improving patient outcomes. Mammography is widely used but has limitations, particularly for younger women with denser breasts. These include reduced sensitivity, false positives, and radiation risks.

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