Novel AI-Based Quantification of Breast Arterial Calcification to Predict Cardiovascular Risk
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| Main Authors: | , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Dapamede, Theodorus Urooj, Aisha Joshi, Vedant Gershon, Gabrielle Li, Frank Chavoshi, Mohammadreza Brown-Mulry, Beatrice Isaac, Rohan Satya Mansuri, Aawez Robichaux, Chad Ayoub, Chadi Arsanjani, Reza Sperling, Laurence Gichoya, Judy van Assen, Marly ONeill, Charles W. Banerjee, Imon Trivedi, Hari |
| author_facet | Dapamede, Theodorus Urooj, Aisha Joshi, Vedant Gershon, Gabrielle Li, Frank Chavoshi, Mohammadreza Brown-Mulry, Beatrice Isaac, Rohan Satya Mansuri, Aawez Robichaux, Chad Ayoub, Chadi Arsanjani, Reza Sperling, Laurence Gichoya, Judy van Assen, Marly ONeill, Charles W. Banerjee, Imon Trivedi, Hari |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14550 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Novel AI-Based Quantification of Breast Arterial Calcification to Predict Cardiovascular Risk Dapamede, Theodorus Urooj, Aisha Joshi, Vedant Gershon, Gabrielle Li, Frank Chavoshi, Mohammadreza Brown-Mulry, Beatrice Isaac, Rohan Satya Mansuri, Aawez Robichaux, Chad Ayoub, Chadi Arsanjani, Reza Sperling, Laurence Gichoya, Judy van Assen, Marly ONeill, Charles W. Banerjee, Imon Trivedi, Hari Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Novel AI-Based Quantification of Breast Arterial Calcification to Predict Cardiovascular Risk |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2503.14550 |