Artificial intelligence-based quantification of breast arterial calcifications to predict cardiovascular morbidity and mortality

Clinical Snapshot

Key Findings

• P Value: P < 0.001 for trend

• Effect Size: Dose-response relationship: mild BAC HR 1.28-1.32, moderate BAC HR 1.75-1.79, severe BAC HR 2.80-3.29

• Primary Outcome: Major adverse cardiovascular events (MACE)

• Nnt Or Sensitivity: Each 1 mm² increase in BAC conferred additional 2-3% risk for MACE

• Confidence Interval: 95% CIs provided for all hazard ratios, ranging from 1.10-5.05 across categories

Clinical Application

High feasibility as leverages existing mammography infrastructure without additional radiation exposure Relevant to BreastScreen Australia program; could enhance cardiovascular risk assessment in primary care aligned with RACGP guidelines for CVD prevention Women aged 40+ undergoing routine mammography screening, particularly those with intermediate cardiovascular risk

Abstract

BACKGROUND AND AIMS: Women are underdiagnosed and undertreated for cardiovascular disease (CVD). Automatic quantification of breast arterial calcification (BAC) on screening mammography can identify women at risk for CVD. This study aimed to determine whether artificial intelligence-based automatic quantification of BAC from screening mammograms predicts CVD and mortality beyond PREVENT scores in a large, racially diverse, multi-institutional population. METHODS: This retrospective cohort study included 123 762 women from two healthcare systems who had screening mammograms. Breast arterial calcification was quantified using a transformer-based neural network for segmentation. Breast arterial calcification severity was categorized as zero (0 mm2), mild (>0-10 mm2), moderate (>10-25 mm2), and severe (>25 mm2). Kaplan-Meier analysis, Cox proportional hazards, and Fine-Gray competing event models were used to examine the association between BAC and major adverse cardiovascular events (MACE). RESULTS: Breast arterial calcification was detected in 16.1% (internal cohort) and 20.6% (external cohort) of women and provided significant prognostic value incremental to the PREVENT score. In PREVENT adjusted models, a clear dose-response was observed. Compared with zero BAC, mild [internal: hazard ratio (HR) 1.32, 95% confidence interval (CI) 1.10-1.59; external: HR 1.28, 95% CI 1.17-1.39], moderate (internal: HR 1.75, 95% CI 1.23-2.50; external: HR 1.79, 95% CI 1.55-2.06), and severe BAC (internal: HR 3.29, 95% CI 2.15-5.05; external: HR 2.80, 95% CI 2.36-3.32) were all prognostic for any MACE. Each 1 mm2 increase in BAC conferred an additional 2%-3% risk for MACE (P < .001). CONCLUSIONS: Automatically quantified BAC is an independent predictor of MACE and mortality, adding prognostic value to the PREVENT score. This approach may provide an opportunistic cardiovascular risk assessment during routine mammography screening without additional radiation exposure to guide earlier and more effective preventive care for women.

References

1. 1.Dapamede, T., Urooj, A., Joshi, V., Gershon, G., Li, F., Chavoshi, M., Brown-Mulry, B., Isaac, R. S., Mansuri, A., Robichaux, C., Ayoub, C., Arsanjani, R., Sperling, L., Gichoya, J., van Assen, M., O'Neill, W. C., Banerjee, I., & Trivedi, H. (2026). Artificial intelligence-based quantification of breast arterial calcifications to predict cardiovascular morbidity and mortality. European Heart Journal. https://doi.org/10.1093/eurheartj/ehag128