Novel AI-Based Quantification of Breast Arterial Calcification to Predict Cardiovascular Risk
Journal:
arXiv
Published Date:
Mar 17, 2025
Abstract
Women are underdiagnosed and undertreated for cardiovascular disease.
Automatic quantification of breast arterial calcification on screening
mammography can identify women at risk for cardiovascular disease and enable
earlier treatment and management of disease. In this retrospective study of
116,135 women from two healthcare systems, a transformer-based neural network
quantified BAC severity (no BAC, mild, moderate, and severe) on screening
mammograms. Outcomes included major adverse cardiovascular events (MACE) and
all-cause mortality. BAC severity was independently associated with MACE after
adjusting for cardiovascular risk factors, with increasing hazard ratios from
mild (HR 1.18-1.22), moderate (HR 1.38-1.47), to severe BAC (HR 2.03-2.22)
across datasets (all p<0.001). This association remained significant across all
age groups, with even mild BAC indicating increased risk in women under 50. BAC
remained an independent predictor when analyzed alongside ASCVD risk scores,
showing significant associations with myocardial infarction, stroke, heart
failure, and mortality (all p<0.005). Automated BAC quantification enables
opportunistic cardiovascular risk assessment during routine mammography without
additional radiation or cost. This approach provides value beyond traditional
risk factors, particularly in younger women, offering potential for early CVD
risk stratification in the millions of women undergoing annual mammography.
