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Automated Breast Arterial Calcification Score Is Associated With Cardiovascular Outcomes and Mortality
Tara Shrout Allen1, Quan M Bui2, Gregory M Petersen3
1Division of Preventive Medicine, University of California-San Diego, La Jolla, California, USA.
Insights
Breast arterial calcification (BAC) on mammograms indicates higher cardiovascular disease (CVD) risk. Quantifying BAC with AI improves risk prediction, especially for younger women.
Area of Science:
- Cardiology
- Radiology
- Artificial Intelligence in Medicine
Background:
- Breast arterial calcification (BAC) on mammograms is a recognized indicator of cardiovascular disease (CVD) risk in women.
- Current limitations exist in quantifying BAC and conducting clinical outcomes studies.
Purpose of the Study:
- To assess the association between the presence and quantity of BAC and CVD outcomes.
- To evaluate the utility of an AI-generated BAC score for risk stratification.
Main Methods:
- Retrospective analysis of screening mammograms from 12,092 women (2007-2016).
- BAC quantified using an AI algorithm, analyzed as binary and continuous variables.
- Regression analyses adjusted for multiple CVD risk factors.
Main Results:
- BAC was present in 23% of women.
- Women with BAC had significantly higher rates of mortality and a composite CVD event.
- Adjusted analyses showed BAC presence (HR 1.49-1.56) and higher BAC scores (HR 1.08 per 10-point increase) were associated with increased risk.
- BAC was particularly predictive in younger women.
Conclusions:
- BAC is an independent predictor of mortality and CVD events, particularly in younger women.
- Quantification of BAC using AI provides incremental risk stratification beyond simple presence.
- AI-based BAC measurement is feasible and clinically relevant for personalized CVD risk assessment.
Background:
Breast arterial calcification (BAC) on mammograms has emerged as a biomarker of women's cardiovascular disease (CVD) risk, but there is a lack of quantification tools and clinical outcomes studies.
Objectives:
This study assessed the association of BAC (both presence and quantity) with CVD outcomes.
Methods:
This single-center, retrospective study included women with a screening mammogram from 2007 to 2016. BAC was quantified using an artificial intelligence-generated score, which was assessed as both a binary and continuous variable. Regression analyses evaluated the association between BAC and mortality and a composite of acute myocardial infarction, heart failure, stroke, and mortality. Analyses were adjusted for age, race, diabetes, smoking, blood pressure, cholesterol, and history of CVD and chronic kidney disease.
Results:
A total of 18,092 women were included in this study (mean age 56.8 ± 11.0 years; diabetes [13%], hypertension [36%], hyperlipidemia [40%], and smoking [5%]). BAC was present in 4,223 (23%). Over a median follow-up of 6 years, death occurred in 7.8% and 2.3% of women with and without BAC, respectively. The composite occurred in 12.4% and 4.3% of women with and without BAC, respectively. Compared to those without, women with BAC had adjusted HRs of 1.49 (95% CI: 1.33-1.67) for mortality and 1.56 (95% CI: 1.41-1.72) for the composite. Each 10-point increase in the BAC score was associated with higher risk of mortality (HR: 1.08 [95% CI: 1.06-1.11]) and the composite (HR: 1.08 [95% CI: 1.06-1.10]). BAC was especially predictive of future events among younger women.
Conclusions:
BAC is independently associated with mortality and CVD, especially among younger women. Measurement of BAC beyond presence adds incremental risk stratification. Quantifying BAC using an artificial intelligence algorithm is feasible, clinically relevant, and may improve personalized CVD risk stratification.
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