Evaluating the efficacy of automated breast arterial calcification quantification models in detecting BAC from mammograms with non-BAC calcifications

Kaier Wang; Aristarkh Tikhonov; Lester Litchfield; Melissa L. Hill

doi:10.1117/12.3023832

29 May 2024 Evaluating the efficacy of automated breast arterial calcification quantification models in detecting BAC from mammograms with non-BAC calcifications

Kaier Wang, Aristarkh Tikhonov, Lester Litchfield, Melissa L. Hill

Author Affiliations +

Proceedings Volume 13174, 17th International Workshop on Breast Imaging (IWBI 2024); 131741U (2024) https://doi.org/10.1117/12.3023832
Event: 17th International Workshop on Breast Imaging (IWBI 2024), 2024, Chicago, IL, United States

Abstract

This study investigates the effectiveness of artificial intelligence (AI)-based models in detecting and quantifying Breast Arterial Calcification (BAC) from mammograms, a potential indicator of cardiovascular disease. Using two distinct subsets from the OPTIMAM database, an enriched dataset of 1683 images previously confirmed by expert readers to have lesions with non-BAC calcifications, and a ‘normal’ dataset with 1401 representative screening mammography exams, selected among those that were negative on both the included and prior exams. Manual annotation of the calcification data by four readers established ground truth. Two novel BAC detection and quantification models were tested, a baseline and enhanced model. The models exhibited promising results, particularly in terms of a low false positive rate for the enhanced model at 0.6%, but also highlighted the need for improvements to achieve a balance between sensitivity (51.0%) and specificity (99.4%). Notably, 62% of the findings missed by the enhanced model were classified as single-wall BAC, which is usually scored as minimal based on a lower association with cardiovascular disease. Future work is required to establish the association of the model performance with clinical outcomes. The study also examined the relationship between BAC prevalence and certain patient characteristics such as age and Volpara^® Density Grade (VDG) in the ‘normal’ screening dataset. Significant correlations were found between BAC volume and patient age, and between BAC prevalence and VDG, which aligns with existing literature. The findings emphasize the potential of AI in improving the consistency of BAC detection with objective quantitative measures, as well as the developed model’s ability to predict the prevalence of BAC in relation to age.

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Kaier Wang, Aristarkh Tikhonov, Lester Litchfield, and Melissa L. Hill "Evaluating the efficacy of automated breast arterial calcification quantification models in detecting BAC from mammograms with non-BAC calcifications", Proc. SPIE 13174, 17th International Workshop on Breast Imaging (IWBI 2024), 131741U (29 May 2024); https://doi.org/10.1117/12.3023832

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KEYWORDS

Breast

Mammography

Breast density

Performance modeling

Artificial intelligence

Cardiovascular disorders

X-rays

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