Novel AI-Based Quantification of Breast Arterial Calcification to Predict Cardiovascular Risk

2025-03-17

AI Summary

Underdiagnosis of cardiovascular disease (CVD) in women remains a critical clinical challenge, particularly for early risk identification. Method: We propose an opportunistic CVD risk assessment paradigm leveraging routine screening mammograms, using an end-to-end Transformer model to automatically quantify breast arterial calcification (BAC) severity (none/mild/moderate/severe) without additional radiation exposure or cost. Contribution/Results: We report the first evidence that even mild BAC confers independent predictive value for CVD in women under 50 years. BAC severity outperforms conventional ASCVD risk scores in early risk stratification, especially among younger women. In a cohort of 116,000 women, BAC severity was significantly associated with major adverse cardiovascular events and all-cause mortality (HR = 1.18–2.22; *p* < 0.001), demonstrating robustness across age groups and healthcare systems.

Technology Category

• Computer Vision: Multi-modal Vision
• Reasoning under Uncertainty: Graphical Models
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Security and Privacy: Data transparency and provenance

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.

Problem

• Automated BAC quantification predicts cardiovascular risk in women.
• BAC severity correlates with major adverse cardiovascular events.
• Opportunistic CVD risk assessment during routine mammography.

Innovation

• Transformer-based neural network for BAC quantification
• Automated BAC severity assessment on mammograms
• Opportunistic CVD risk prediction during mammography