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Purpose: There are insufficient large-scale studies comparing the performance of screening mammography in women of different races. This study aims to compare the screening performance metrics across racial and age groups in the National Mammography Database (NMD).
Methods: All screening mammograms performed between January 1, 2008, and December 31, 2021, in women aged 30-100 years from 746 mammography facilities in 46 U.S. states in the NMD were included. Patients were stratified by 10-year age intervals and 5 racial groups (African American, American Indian, Asian, White, unknown). Incidence of risk factors (breast density, personal history, family history of breast cancer, age), and time since prior exams were compared. Five screening mammography metrics were calculated: recall rate (RR), cancer detection rate (CDR), positive predictive values for recalls (PPV), biopsy recommended (PPV) and biopsy performed (PPV).
Results: 29,479,655 screening mammograms performed in 13,181,241 women between January 1, 2008, and December 31, 2021, from the NMD were analyzed. The overall mean performance metrics were RR 10.00% (95% CI 9.99-10.02), CDR 4.18/1000 (4.16-4.21), PPV 4.18% (4.16-4.20), PPV 25.84% (25.72-25.97), PPV 25.78% (25.66-25.91). With advancing age, RR significantly decreases, while CDR, PPV, PPV, and PPV significantly increase. Incidence of personal/family history of breast cancer, breast density, age, prior mammogram availability, and time since prior mammogram were mostly similar across all races. Compared to White women, African American women had significantly higher RR, but lower CDR, PPV, PPV and PPV.
Conclusions: Benefits of screening mammography increase with age, including for women age > 70 and across all races. Screening mammography is effective; with lower RR and higher CDR, PPV, and PPV with advancing age. African American women have poorer outcomes from screening mammography (higher RR and lower CDR), compared to White and all women in the NMD. Racial disparity can be partly explained by higher rate of African American women lost to follow up.
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http://dx.doi.org/10.1007/s10549-023-07124-6 | DOI Listing |
J Healthc Sci Humanit
January 2024
Atlanta VA Medical Center, Atlanta, GA.
The 2019 novel coronavirus disease (COVID-19) has brought to the forefront racial disparities in health outcomes across the US, but there is limited formal analysis into factors associated with these disparities. In-depth examination of COVID-19 disparities has been challenging due to inconsistent case definition, isolation procedures, and incomplete racial and medical information. As of June 2020, over 14,000 (25%) confirmed COVID-19 cases in Georgia did not have racial information.
View Article and Find Full Text PDFMed Phys
September 2025
Department of Radiology, Stony Brook University, New York, USA.
Background: In contrast-enhanced digital mammography (CEDM) and contrast-enhanced digital breast tomosynthesis (CEDBT), low-energy (LE) and high-energy (HE) images are acquired after injection of iodine contrast agent. Weighted subtraction is then applied to generate dual-energy (DE) images, where normal breast tissues are suppressed, leaving iodinated objects enhanced. Currently, clinical systems employ a dual-shot (DS) method, where LE and HE images are acquired with two separate exposures.
View Article and Find Full Text PDFComput Methods Programs Biomed
September 2025
Electrical and Computer Engineering Department, School of Engineering, Morgan State University, Baltimore, MD, 21251, USA. Electronic address:
Breast Cancer (BC) remains a leading cause of morbidity and mortality among women globally, accounting for 30% of all new cancer cases (with approximately 44,000 women dying), according to recent American Cancer Society reports. Therefore, accurate BC screening, diagnosis, and classification are crucial for timely interventions and improved patient outcomes. The main goal of this paper is to provide a comprehensive review of the latest advancements in BC detection, focusing on diagnostic BC imaging, Artificial Intelligence (AI) driven analysis, and health disparity considerations.
View Article and Find Full Text PDFComput Methods Programs Biomed
August 2025
Zhengzhou University, School of Computer and Artificial Intelligence, Zhengzhou, 450001, China. Electronic address:
Background And Objective: The early detection of breast cancer plays a critical role in improving survival rates and facilitating precise medical interventions. Therefore, the automated identification of breast abnormalities becomes paramount, significantly enhancing the prospects of successful treatment outcomes. To address this imperative, our research leverages multiple modalities such as MRI, CT, and mammography to detect and screen for breast cancer.
View Article and Find Full Text PDFJ Med Screen
September 2025
The Cancer Registry of Norway, Department of Screening programs, Norwegian Institute of Public Health, Oslo, Norway.
ObjectiveTo study the implications of implementing artificial intelligence (AI) as a decision support tool in the Norwegian breast cancer screening program concerning cost-effectiveness and time savings for radiologists.MethodsIn a decision tree model using recent data from AI vendors and the Cancer Registry of Norway, and assuming equal effectiveness of radiologists plus AI compared to standard practice, we simulated costs, effects and radiologist person-years over the next 20 years under different scenarios: 1) Assuming a €1 additional running cost of AI instead of the €3 assumed in the base case, 2) varying the AI-score thresholds for single vs. double readings, 3) varying the consensus and recall rates, and 4) reductions in the interval cancer rate compared to standard practice.
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