Artificial Intelligence Software Demonstrates High Performance for Breast Arterial Calcification Detection on Mammography

Goel, Vinay; Cameron, William; Spear, E.; Sultana, N.; Chan, Jasmine; Cheen, Y. Chee; Tan, Sean; Tan, Chew-Lim; Mantey, R.; Brown, Adam; Nerlekar, Nitesh

doi:10.1016/j.hlc.2023.06.504

Cited by 3 publications

(2 citation statements)

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“…In the present study, BAC was quantified using an automated method driven by a trained deep neural AI network, recently validated with high diagnostic performance. 8 Other machine learning techniques have been developed for BAC quantification, including a densitometry method, and have been validated prospectively. 10 Such studies have assessed methods of BAC quantification, though await association with clinical outcomes.…”

Section: Discussionmentioning

confidence: 99%

“…BAC was quantified using a validated, proprietary software (cmAngio™, CureMetrix) based on a deep neural, AI network and previously trained using over 21,000 2D full-field digital mammograms (FFDM) obtained from multiple sites in Australia, Brazil, and the United States (not including UC San Diego Health). 8 For the present study, four FFDM images from each participant were used. The software cmAngio™ assesses screening mammography images and feeds them through the deep learning model to identify regions of interest within the breast.…”

Section: Evaluation Of Bacmentioning

confidence: 99%

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Association of an Automated Breast Arterial Calcification Score with Cardiovascular Outcomes and Mortality

Bui,

Allen,

Mantey

et al. 2023

Preprint

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BackgroundBreast arterial calcification (BAC), visible on mammograms, has emerged as a biomarker of cardiovascular disease (CVD) in women. Barriers to clinical implementation of BAC include limited studies with clinical outcomes and lack of quantification tools.MethodsThis single-center, retrospective study included women with a screening digital mammogram from 2008-2016. BAC was quantified using an automated, artificial intelligence (AI)-generated Bradley score, as a binary (Bradley score ≥5) and continuous variable. Clinical outcomes were determined via electronic medical records. Regression analyses were used to evaluate the association between BAC and outcomes of mortality and a composite of acute myocardial infarction, heart failure, stroke, and mortality. Models were adjusted for age, race, diabetes, smoking status, blood pressure, cholesterol, and history of CVD and chronic kidney disease.ResultsA total of 18,092 women were included with a mean age 56.8±11.0 years. Prevalence of comorbidities included diabetes (13%), hypertension (36%), hyperlipidemia (40%) and smoking (5%). BAC was present in 4,223 (23%). Over a median follow-up of 6 years, death occurred in 7.8% women with and 2.3% women without BAC. The composite outcome occurred in 12.4% of women with and 4.3% of women without BAC. Compared to those without, women with BAC had adjusted hazard ratios (aHR) of 1.49 (95% CI 1.33-1.67) for mortality and 1.56 (1.41-1.72) for the composite endpoint, after accounting for traditional risk factors. With a continuous BAC score, each 10-point increase was associated with higher risk of mortality (1.08 [1.06-1.11]) and the composite endpoint (1.08 [1.06-1.10]). BAC was especially predictive of future events among younger women.ConclusionBAC is significantly and independently associated with mortality and incident CVD, especially among younger women. Measuring BAC using an AI algorithm is feasible and clinically relevant. Further studies are needed to confirm these findings and to evaluate whether interventions guided by BAC improve outcomes.CLINICAL PERSPECTIVEWhat is new?Breast arterial calcification (BAC) on mammograms can be reliably quantified using a novel software based on an artificial intelligence (AI) algorithm.BAC is independently associated with an increased risk of all-cause mortality and cardiovascular outcomes. These associations held true when looking at BAC as presence, score quartile, and as a continuous value as well as after accounting for traditional cardiovascular risk factors.In stratification analysis, BAC was most predictive of all-cause mortality and cardiovascular outcomes among younger women (age 40-59 years), but still independently predictive in women aged 60-74 years.What are the clinical implications?Our data provide support for the inclusion of BAC findings on mammogram reports.Automated quantification tools and reporting methods of BAC will be critical to engagement of radiologists and implementation of reporting.While additional studies are needed to determine the appropriate clinical response, the presence of BAC should at the minimum stimulate patient-provider conversations on lifestyle changes to mitigate cardiovascular risk, especially among younger women.

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Section: Discussionmentioning

confidence: 99%

Section: Evaluation Of Bacmentioning

confidence: 99%

Association of an Automated Breast Arterial Calcification Score with Cardiovascular Outcomes and Mortality

Bui,

Allen,

Mantey

et al. 2023

Preprint

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Automated Breast Arterial Calcification Score Is Associated With Cardiovascular Outcomes and Mortality

Allen,

Bui,

Petersen

et al. 2024

JACC: Advances

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

Wang,

Tikhonov,

Litchfield

et al. 2024

17th International Workshop on Breast Imaging (IWBI 2024)

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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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Artificial Intelligence Software Demonstrates High Performance for Breast Arterial Calcification Detection on Mammography

Cited by 3 publications

References 0 publications

Association of an Automated Breast Arterial Calcification Score with Cardiovascular Outcomes and Mortality

Association of an Automated Breast Arterial Calcification Score with Cardiovascular Outcomes and Mortality

Automated Breast Arterial Calcification Score Is Associated With Cardiovascular Outcomes and Mortality

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

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