Breast arterial calcifications on mammography: a new marker of cardiovascular risk in asymptomatic middle age women?

Minssen, Lise; T, Dao; Quang, An Vo; Martín, Laura Hervás; Andureau, Etienne; Luciani, Alain; Meyblum, Evelyne; Dérumeaux, Geneviève; Deux, Jean-François

doi:10.1007/s00330-022-08571-3

Cited by 10 publications

(5 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…al., who found that the diagnostic accuracy (~84%) for BAC with CAC was the highest in patients under the age of 60 years. 14 These findings are important, since they suggest that early risk-stratification with BAC in younger women may help identify new candidates for lifestyle modification and preventative therapies, and may ultimately help improve their outcomes. Indeed, results from this study and others suggest that BAC may develop at an earlier age than other traditional cardiovascular risk factors, and thus could serve as an early sign of underlying ASCVD risk.…”

Section: Discussionmentioning

confidence: 99%

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

Bui,

Allen,

Mantey

et al. 2023

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

Bui,

Allen,

Mantey

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Traditional risk scores such as the Framingham score often underestimate the risk in women, leading to missed opportunities for early diagnosis and appropriate primary prevention [ 3 – 6 ]. Over the past decades, breast arterial calcifications (BAC) have been advocated as a promising sex-specific biomarker of CVD to improve women’s cardiovascular stratification [ 7 – 10 ]. BAC are medial calcium depositions detectable as parallel line opacities on about 13% of routine mammograms [ 11 , 12 ] and have been shown to be associated with an elevated hazard of CVD, independent of most conventional risk factors such as smoking [ 13 – 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…Nonetheless, the authors of the MINERVA study (a prospective study on a cohort of over 5,000 women with a follow-up of over 5 years) [ 3 ] did not observe a quantitative association between BAC burden and hard atherosclerotic CVD events; however, they identified a threshold effect for global CVD in women over the 95th percentile of BAC. With the increasing use of mammography for breast cancer screening, BAC present an opportunity for CVD risk stratification in asymptomatic women [ 10 , 18 ]. Nevertheless, their assessment is a time-consuming manual task, vulnerable to intra- and inter-observer variability [ 19 , 20 ]; also, the considerable diversity of BAC’s appearance and the lack of a standard reporting guideline limited their adoption as a robust imaging biomarker in clinical practice [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Deep transfer learning for detection of breast arterial calcifications on mammograms: a comparative study

Mobini,

Capra,

Colarieti

et al. 2024

Eur Radiol Exp

View full text Add to dashboard Cite

Introduction Breast arterial calcifications (BAC) are common incidental findings on routine mammograms, which have been suggested as a sex-specific biomarker of cardiovascular disease (CVD) risk. Previous work showed the efficacy of a pretrained convolutional network (CNN), VCG16, for automatic BAC detection. In this study, we further tested the method by a comparative analysis with other ten CNNs. Material and methods Four-view standard mammography exams from 1,493 women were included in this retrospective study and labeled as BAC or non-BAC by experts. The comparative study was conducted using eleven pretrained convolutional networks (CNNs) with varying depths from five architectures including Xception, VGG, ResNetV2, MobileNet, and DenseNet, fine-tuned for the binary BAC classification task. Performance evaluation involved area under the receiver operating characteristics curve (AUC-ROC) analysis, F1-score (harmonic mean of precision and recall), and generalized gradient-weighted class activation mapping (Grad-CAM++) for visual explanations. Results The dataset exhibited a BAC prevalence of 194/1,493 women (13.0%) and 581/5,972 images (9.7%). Among the retrained models, VGG, MobileNet, and DenseNet demonstrated the most promising results, achieving AUC-ROCs > 0.70 in both training and independent testing subsets. In terms of testing F1-score, VGG16 ranked first, higher than MobileNet (0.51) and VGG19 (0.46). Qualitative analysis showed that the Grad-CAM++ heatmaps generated by VGG16 consistently outperformed those produced by others, offering a finer-grained and discriminative localization of calcified regions within images. Conclusion Deep transfer learning showed promise in automated BAC detection on mammograms, where relatively shallow networks demonstrated superior performances requiring shorter training times and reduced resources. Relevance statement Deep transfer learning is a promising approach to enhance reporting BAC on mammograms and facilitate developing efficient tools for cardiovascular risk stratification in women, leveraging large-scale mammographic screening programs. Key points • We tested different pretrained convolutional networks (CNNs) for BAC detection on mammograms. • VGG and MobileNet demonstrated promising performances, outperforming their deeper, more complex counterparts. • Visual explanations using Grad-CAM++ highlighted VGG16’s superior performance in localizing BAC. Graphical Abstract

show abstract

“…1 ) [ 8 , 12 ]. Their approximate prevalence, although in a wide range, has been estimated around 13% [ 11 , 13 – 16 ]. BAC presence has been associated with a 1.23 increased risk of CVD in postmenopausal women [ 14 ] and has higher diagnostic accuracy than other traditional cardiovascular risk factors in asymptomatic middle-aged women, especially under 60 years of age [ 10 , 11 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Detection and quantification of breast arterial calcifications on mammograms: a deep learning approach

et al. 2023

View full text Add to dashboard Cite

Objective Breast arterial calcifications (BAC) are a sex-specific cardiovascular disease biomarker that might improve cardiovascular risk stratification in women. We implemented a deep convolutional neural network for automatic BAC detection and quantification. Methods In this retrospective study, four readers labelled four-view mammograms as BAC positive (BAC+) or BAC negative (BAC−) at image level. Starting from a pretrained VGG16 model, we trained a convolutional neural network to discriminate BAC+ and BAC− mammograms. Accuracy, F1 score, and area under the receiver operating characteristic curve (AUC-ROC) were used to assess the diagnostic performance. Predictions of calcified areas were generated using the generalized gradient-weighted class activation mapping (Grad-CAM++) method, and their correlation with manual measurement of BAC length in a subset of cases was assessed using Spearman ρ. Results A total 1493 women (198 BAC+) with a median age of 59 years (interquartile range 52–68) were included and partitioned in a training set of 410 cases (1640 views, 398 BAC+), validation set of 222 cases (888 views, 89 BAC+), and test set of 229 cases (916 views, 94 BAC+). The accuracy, F1 score, and AUC-ROC were 0.94, 0.86, and 0.98 in the training set; 0.96, 0.74, and 0.96 in the validation set; and 0.97, 0.80, and 0.95 in the test set, respectively. In 112 analyzed views, the Grad-CAM++ predictions displayed a strong correlation with BAC measured length (ρ = 0.88, p < 0.001). Conclusion Our model showed promising performances in BAC detection and in quantification of BAC burden, showing a strong correlation with manual measurements. Clinical relevance statement Integrating our model to clinical practice could improve BAC reporting without increasing clinical workload, facilitating large-scale studies on the impact of BAC as a biomarker of cardiovascular risk, raising awareness on women’s cardiovascular health, and leveraging mammographic screening. Key Points • We implemented a deep convolutional neural network (CNN) for BAC detection and quantification. • Our CNN had an area under the receiving operator curve of 0.95 for BAC detection in the test set composed of 916 views, 94 of which were BAC+ . • Furthermore, our CNN showed a strong correlation with manual BAC measurements (ρ = 0.88) in a set of 112 views.

show abstract

Breast arterial calcifications on mammography: a new marker of cardiovascular risk in asymptomatic middle age women?

Cited by 10 publications

References 28 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

Deep transfer learning for detection of breast arterial calcifications on mammograms: a comparative study

Detection and quantification of breast arterial calcifications on mammograms: a deep learning approach

Contact Info

Product

Resources

About