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Artificial intelligence-based quantification of breast arterial calcifications to predict cardiovascular morbidity
Theodorus Dapamede1, Aisha Urooj2, Vedant Joshi2
1Department of Radiology, Emory University, Atlanta, GA, USA.
Insights
Artificial intelligence detects breast arterial calcification (BAC) on mammograms, predicting cardiovascular disease (CVD) risk. This AI-enhanced risk assessment offers valuable insights for preventive care in women.
Area of Science:
- Cardiology
- Radiology
- Artificial Intelligence
Background:
- Cardiovascular disease (CVD) is underdiagnosed and undertreated in women.
- Breast arterial calcification (BAC) on mammography can indicate CVD risk.
- Current risk assessment tools may not fully capture CVD risk in women.
Purpose of the Study:
- To evaluate the efficacy of AI-based automatic quantification of BAC from screening mammograms in predicting CVD and mortality.
- To determine if AI-quantified BAC provides prognostic value beyond existing PREVENT scores.
- To assess the predictive capability in a large, diverse, multi-institutional population.
Main Methods:
- Retrospective cohort study of 123,762 women with screening mammograms.
- Utilized a transformer-based neural network for automatic BAC quantification.
- Employed Kaplan-Meier analysis, Cox proportional hazards, and Fine-Gray models to assess BAC association with major adverse cardiovascular events (MACE).
Main Results:
- BAC was detected in 16.1% (internal) and 20.6% (external) of women.
- AI-quantified BAC demonstrated significant prognostic value incremental to the PREVENT score.
- Increased BAC severity correlated with higher MACE risk (e.g., severe BAC HR 3.29 in internal cohort).
- Each 1 mm² increase in BAC conferred an additional 2%-3% risk for MACE (P < .001).
Conclusions:
- Automated BAC quantification via AI is an independent predictor of MACE and mortality.
- This AI-driven approach enhances CVD risk prediction beyond the PREVENT score.
- Opportunistic cardiovascular risk assessment during mammography can guide earlier preventive care without extra radiation.
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.
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