Beyond Breast Cancer Detection: AI reveals Hidden Cardiovascular Risk in Mammograms
For decades, mammograms have been a cornerstone of women’s health, primarily focused on early breast cancer detection. Though, a groundbreaking new study reveals that these routine scans hold a wealth of untapped data – a potential early warning system for cardiovascular disease, teh leading cause of death for women in the United States. Researchers at Emory University and Mayo Clinic have developed an artificial intelligence (AI) model capable of analyzing breast arterial calcification (BAC) visible on mammograms, translating this data into a personalized cardiovascular risk score. This innovation promises to revolutionize preventative cardiology, notably for younger women where early intervention can be most impactful.
The Silent Indicator: Breast Arterial Calcification and Heart Health
Calcium buildup in arteries isn’t limited to the heart; it can also occur in the arteries within breast tissue. This BAC, visible as bright pixels on mammogram X-rays, is a strong indicator of underlying cardiovascular damage and a predictor of future heart disease and stroke. previous research has demonstrated a significant link: women with detectable BAC face a 51% higher risk of experiencing cardiovascular events. Despite this established connection, radiologists traditionally haven’t quantified or reported BAC levels to patients or their physicians.
“We see an opportunity for women to get screened for cancer and additionally get a cardiovascular screen from their mammograms,” explains Dr.Theo Dapamede, lead author of the study and a postdoctoral fellow at Emory University. “our study showed that breast arterial calcification is a good predictor for cardiovascular disease,especially in patients younger than age 60.If we are able to screen and identify these patients early, we can refer them to a cardiologist for further risk assessment.”
How the AI Model Works: A New Level of Image Analysis
This isn’t simply about detecting BAC; its about quantifying it and translating it into actionable risk assessment. The research team developed a “deep-learning” AI model specifically trained to:
Segment Calcified Vessels: Precisely identify and outline calcified arteries within mammogram images. This segmentation approach distinguishes this model from previous attempts at analyzing BAC.
Calculate Cardiovascular risk: Leverage a massive dataset – images and electronic health records from over 56,000 patients who underwent mammograms at emory Healthcare between 2013 and 2020 with at least five years of follow-up data – to correlate BAC levels with future cardiovascular events. Provide risk Categorization: Classify patients as having low, moderate, or severe cardiovascular risk based on their BAC levels.This complex approach allows for a more nuanced understanding of a patient’s cardiovascular health than previously possible from a standard mammogram.
Key Findings: Early Warning for Younger Women
The study’s results are compelling. Researchers found a clear correlation between BAC levels and the risk of cardiovascular events, particularly in women under 60 and between 60-80 years old.
Significant Risk increase: Women with the highest levels of BAC (above 40 mm2) experienced a substantially lower five-year survival rate (86.4%) compared to those with the lowest levels (below 10 mm2) – a staggering 2.8 times higher risk of death within five years. Early Intervention Potential: The model’s ability to identify risk in younger women is particularly significant. Early detection allows for proactive interventions like lifestyle modifications, medication, and closer monitoring, potentially preventing or delaying the onset of heart disease.
Limited Benefit for Older Patients: The correlation between BAC and risk diminished in women over 80, suggesting the tool is most valuable for earlier risk assessment.
The Future of Mammogram Screening: From Cancer Detection to Comprehensive Health Assessment
While the AI model is currently not available for clinical use, the potential impact is substantial. Researchers are working towards external validation and FDA approval, paving the way for widespread adoption by healthcare systems.
“Advances in deep learning and AI have made it much more feasible to extract and use more information from images to inform opportunistic screening,” says Dr. Dapamede.Beyond cardiovascular disease, the team is exploring the possibility of using similar AI models to identify biomarkers for other conditions, such as peripheral artery disease and kidney disease, from mammogram images.This represents a paradigm shift in how we view mammography – moving beyond a single-purpose screening tool to a comprehensive assessment of women’s overall health.
Sources:
Emory University. (Date of Publication). AI model can assess cardiovascular risk from mammograms.* [https://news.emory.edu/stories/2024/02/ai-model-cardiovascular-risk-mammograms/index.html](https://news.emory.edu/stories/2024/02/ai-model-cardiovascular-risk