Associations of Breast Arterial Calcifications with Cardiovascular Disease

Ibrahim, Mu'ath; Suleiman, Moayyad E.; Gandomkar, Ziba; Taba, Amir Tavakoli; Arnott, Clare; Jorm, Louisa; Barraclough, J.; Barbieri, Sebastiano; Brennan, Patrick C.

doi:10.1089/jwh.2022.0394

Cited by 2 publications

(3 citation statements)

References 110 publications

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“…The prevalence of BAC in the population under study was found to be approximately 27%, which resonates with the percentages reported in similar age groups within the existing literature [5]. Our investigation into the relationship between BAC and CVD revealed a notable association.…”

Section: Statistical Analysis Resultssupporting

confidence: 88%

“…Breast arterial calcification (BAC), a common incidental finding in mammographic screenings, has been increasingly recognized for its potential as a CVD marker [5]. The clinical significance of BAC, however, was not fully appreciated until its correlation with diabetes mellitus was identified in the 1980s [6].…”

Section: Introductionmentioning

confidence: 99%

“…The clinical significance of BAC, however, was not fully appreciated until its correlation with diabetes mellitus was identified in the 1980s [6]. Subsequent research has suggested a positive association between BAC and CVD, as well as coronary artery disease [5].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep learning analysis of breast arterial calcifications: a study on predicting cardiovascular disease in women

Ibrahim,

Gandomkar,

Suleiman

et al. 2024

Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment

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Breast arterial calcifications (BAC) are increasingly recognized as indicative markers for cardiovascular disease (CVD). In this study, we manually annotated BAC areas on 3,330 mammograms, forming the foundational dataset for developing a deep learning model to automate assessment of BAC. Using this annotated data, we propose a semi-supervised deep learning approach to analyze unannotated mammography images, leveraging both labeled and unlabeled data to improve BAC segmentation accuracy. Our approach combines the U-net architecture, a well-established deep learning method for medical image segmentation, with a semi-supervised learning technique. We retrieved mammographic examinations of 6,000 women (3,000 with confirmed CVD and 3,000 without) from the screening archive to allow for a focused study. Utilizing our trained deep learning model, we accurately detected and measured the severity of BAC in these mammograms. Additionally, we examined the time between mammogram screenings and the occurrence of CVD events. Our study indicates that both the presence and severity (grade) of BAC, identified and measured using deep learning for automated segmentation, are crucial for primary CVD prevention. These findings underscore the value of technology in understanding the link between BAC in mammograms and cardiovascular disease, shaping future screening and prevention strategies for women's health.

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Section: Statistical Analysis Resultssupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep learning analysis of breast arterial calcifications: a study on predicting cardiovascular disease in women

Ibrahim,

Gandomkar,

Suleiman

et al. 2024

Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment

Self Cite

View full text Add to dashboard Cite

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Phenotypic switch of vascular smooth muscle cells in COVID‐19: Role of cholesterol, calcium, and phosphate

Ghanem,

Essayli,

Kotaich

et al. 2024

Journal Cellular Physiology

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Although the novel coronavirus disease 2019 (COVID‐19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), primarily manifests as severe respiratory distress, its impact on the cardiovascular system is also notable. Studies reveal that COVID‐19 patients often suffer from certain vascular diseases, partly attributed to increased proliferation or altered phenotype of vascular smooth muscle cells (VSMCs). Although the association between COVID‐19 and VSMCs is recognized, the precise mechanism underlying SARS‐CoV‐2's influence on VSMC phenotype remains largely under‐reviewed. In this context, while there is a consistent body of literature dissecting the effect of COVID‐19 on the cardiovascular system, few reports delve into the potential role of VSMC switching in the pathophysiology associated with COVID‐19 and the molecular mechanisms involved therein. This review dissects and critiques the link between COVID‐19 and VSMCs, with particular attention to pathways involving cholesterol, calcium, and phosphate. These pathways underpin the interaction between the virus and VSMCs. Such interaction promotes VSMC proliferation, and eventually potentiates vascular calcification as well as worsens prognosis in patients with COVID‐19.

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Associations of Breast Arterial Calcifications with Cardiovascular Disease

Cited by 2 publications

References 110 publications

Deep learning analysis of breast arterial calcifications: a study on predicting cardiovascular disease in women

Deep learning analysis of breast arterial calcifications: a study on predicting cardiovascular disease in women

Phenotypic switch of vascular smooth muscle cells in COVID‐19: Role of cholesterol, calcium, and phosphate

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