AI-driven quantification of breast arterial calcification (BAC) on mammograms predicts risk of major cardiovascular events in women, independent of traditional risk scores.
Key Details
- 1Study used a transformer-based segmentation model to quantify BAC on mammograms from 123,762 women across Emory Healthcare and Mayo Clinic Enterprise.
- 2BAC severity (mild, moderate, severe) was associated with incrementally higher hazard ratios for major adverse cardiovascular events (up to HR 3.29 for severe BAC in the Emory cohort).
- 3Incidence of major cardiovascular events increased more than 8-fold from zero to severe BAC category in the Emory cohort.
- 4Dose-response noted: each 1 mm² increase in BAC conferred an additional 2–3% risk for major events (p < 0.001).
- 5AI-derived BAC scores add prognostic value beyond the PREVENT cardiovascular risk calculator, offering a non-invasive risk assessment using existing mammogram data.
Why It Matters
Opportunistically assessing cardiovascular risk in women using routine mammograms through AI enables earlier prevention without extra imaging or radiation, potentially transforming population health strategies and radiology's role in preventive care.
Source
AuntMinnie
Related News
•Radiology Business
Radiologist-AI Collaboration Boosts Pulmonary Embolism Detection on CTPA
Combining AI with radiologists substantially improves detection of pulmonary embolism (PE) on CT pulmonary angiography (CTPA) scans, new research shows.
•AuntMinnie
MRI Foundation Models and Standardization Featured at ISMRM 2026
Researchers at ISMRM 2026 presented advances in MRI AI foundation models, emphasizing standardization, multimodal analysis, and the promising OmniMRI framework.
•AuntMinnie
Deep Learning Model Predicts Brain Tumor MRI Enhancement Without Gadolinium
German researchers developed a deep learning approach to predict MRI contrast enhancement in brain tumors without the need for gadolinium-based agents.