Detecting Cardiovascular Disease from Mammograms With Deep Learning

Coronary artery disease is a major cause of death in women. Breast arterial calcifications (BACs), detected inmammograms, can be useful riskmarkers associated with the disease. We investigate the feasibility of automated and accurate detection ofBACsinmammograms for risk assessment of coronary artery disease. We develop a 12-layer convolutional neural network to discriminate BAC from non-BAC and apply a pixelwise, patch-based procedure for BAC detection. To assess the performance of the system, we conduct a reader study to provide ground-truth information using the consensus of human expert radiologists. We evaluate the performance using a set of 840 full-field digital mammograms from 210 cases, using both free-responsereceiveroperatingcharacteristic (FROC) analysis and calcium mass quantification analysis. The FROC analysis shows that the deep learning approach achieves a level of detection similar to the human experts. The calcium mass quantification analysis shows that the inferred calcium mass is close to the ground truth, with a linear regression between them yielding a coefficient of determination of 96.24%. Taken together, these results suggest that deep learning can be used effectively to develop an automated system for BAC detection inmammograms to help identify and assess patients with cardiovascular risks

Detecting Cardiovascular Disease from Mammograms With Deep Learning

Coronary artery disease is a major cause of death in women. Breast arterial calcifications (BACs), detected inmammograms, can be useful riskmarkers associated with the disease. We investigate the feasibility of automated and accurate detection ofBACsinmammograms for risk assessment of coronary artery disease. We develop a 12-layer convolutional neural network to discriminate BAC from non-BAC and apply a pixelwise, patch-based procedure for BAC detection. To assess the performance of the system, we conduct a reader study to provide ground-truth information using the consensus of human expert radiologists. We evaluate the performance using a set of 840 full-field digital mammograms from 210 cases, using both free-responsereceiveroperatingcharacteristic (FROC) analysis and calcium mass quantification analysis. The FROC analysis shows that the deep learning approach achieves a level of detection similar to the human experts. The calcium mass quantification analysis shows that the inferred calcium mass is close to the ground truth, with a linear regression between them yielding a coefficient of determination of 96.24%. Taken together, these results suggest that deep learning can be used effectively to develop an automated system for BAC detection inmammograms to help identify and assess patients with cardiovascular risks

Breast Arterial Calcification Is Not Associated with Mild Cognitive Impairment or Incident All-Cause Dementia Among Postmenopausal Women: The MINERVA Study

Background: Since vascular risk factors are implicated in cognitive decline, and breast arterial calcification (BAC) is related to vascular risk, we postulated that BAC may be associated with cognitive impairment and dementia. Methods: We used a multiethnic cohort of 3,913 asymptomatic women 60-79 years of age recruited after mammography screening at a large health plan in 2012-2015. A BAC mass score (mg) was derived from digital mammograms. Cognitive function was measured at baseline using the Montreal Cognitive Assessment (MoCA) and incident all-cause dementia (n = 49 events; median follow-up = 5.6 years) were ascertained with validated ICD-9 and ICD-10 codes. We used cross-sectional linear regression of MoCA scores on BAC, then multinomial logistic regression predicting mild cognitive impairment not progressing to dementia and incident all-cause dementia and, finally, Cox regression of incident all-cause dementia. Results: No association by linear regression was found between MoCA scores and BAC presence in unadjusted or adjusted analysis. Women with severe (upper tertile) BAC had a MoCA score lower by 0.58 points (standard error [SE] = 0.18) relative to women with no BAC. However, this difference disappeared after multivariate adjustment. No significant associations were found in multinomial logistic regression for either BAC presence or gradation in unadjusted or adjusted analysis. No significant associations were found between BAC presence with incident all-cause dementia (fully adjusted hazard ratio = 0.74; 95% confidence interval: 0.39-1.39). Likewise, no significant association with incident all-cause dementia was noted for BAC gradation. Conclusions: Our results do not support the hypothesis that BAC presence or gradation may contribute to cognitive impairment or development of all-cause dementia

No Association Between Bone Mineral Density and Breast Arterial Calcification Among Postmenopausal Women.

Context:The association between bone mineral density (BMD) and breast arterial calcification (BAC) remains poorly understood and controversial. Objective:The objective of this article is to examine the association between BMD and BAC in a large cohort of postmenopausal women undergoing routine mammography. Design:A cross-sectional analysis of baseline data from a multiethnic cohort was performed. Setting:The setting for this analysis is an integrated health care delivery system in Northern California in the United States. Patients:A total of 1273 women age 60 to 79 years (mean age, 67 years) were recruited within 12 months of screening mammography. Main outcome measure:A BAC score (mg) was obtained from digital mammograms using a novel densitometry method. BAC presence was defined as a BAC score greater than 0 mg, and severe BAC as a BAC score greater than 20 mg. Results:Overall, 53% of women had osteopenia and 21% had osteoporosis. The prevalence of BAC greater than 0 mg was 29%, 30%, and 29% among women with normal BMD, osteopenia, and osteoporosis, respectively (P = 0.98). The prevalence of BAC greater than 20 mg was 5%, 3%, and 5% among women with normal BMD, osteopenia and osteoporosis, respectively (P = .65). The odds ratios (ORs) of BAC greater than 0 mg vs BAC = 0 mg after multivariable adjustment were 1.09 (95% CI, 0.81-1.48; P = .54) for osteopenia and 0.99 (95% CI, 0.69-1.48; P = .98) for osteoporosis. The adjusted ORs for BAC greater than 20 mg vs BAC 20 mg or less were 1.03 (95% CI, 0.52-2.01; P = .93) for osteopenia and 1.89 (95 CI, 0.81-4.47; P = .14) for osteoporosis. Conclusion:Our findings do not support an association of either osteopenia or osteoporosis with BAC presence or severity among postmenopausal women

No Association Between Bone Mineral Density and Breast Arterial Calcification Among Postmenopausal Women.

ContextThe association between bone mineral density (BMD) and breast arterial calcification (BAC) remains poorly understood and controversial.ObjectiveThe objective of this article is to examine the association between BMD and BAC in a large cohort of postmenopausal women undergoing routine mammography.DesignA cross-sectional analysis of baseline data from a multiethnic cohort was performed.SettingThe setting for this analysis is an integrated health care delivery system in Northern California in the United States.PatientsA total of 1273 women age 60 to 79 years (mean age, 67 years) were recruited within 12 months of screening mammography.Main outcome measureA BAC score (mg) was obtained from digital mammograms using a novel densitometry method. BAC presence was defined as a BAC score greater than 0 mg, and severe BAC as a BAC score greater than 20 mg.ResultsOverall, 53% of women had osteopenia and 21% had osteoporosis. The prevalence of BAC greater than 0 mg was 29%, 30%, and 29% among women with normal BMD, osteopenia, and osteoporosis, respectively (P = 0.98). The prevalence of BAC greater than 20 mg was 5%, 3%, and 5% among women with normal BMD, osteopenia and osteoporosis, respectively (P = .65). The odds ratios (ORs) of BAC greater than 0 mg vs BAC = 0 mg after multivariable adjustment were 1.09 (95% CI, 0.81-1.48; P = .54) for osteopenia and 0.99 (95% CI, 0.69-1.48; P = .98) for osteoporosis. The adjusted ORs for BAC greater than 20 mg vs BAC 20 mg or less were 1.03 (95% CI, 0.52-2.01; P = .93) for osteopenia and 1.89 (95 CI, 0.81-4.47; P = .14) for osteoporosis.ConclusionOur findings do not support an association of either osteopenia or osteoporosis with BAC presence or severity among postmenopausal women

Kidney function, proteinuria and breast arterial calcification in women without clinical cardiovascular disease: The MINERVA study.

BackgroundBreast arterial calcification (BAC) may be a predictor of cardiovascular events and is highly prevalent in persons with end-stage kidney disease. However, few studies to date have examined the association between mild-to-moderate kidney function and proteinuria with BAC.MethodsWe prospectively enrolled women with no prior cardiovascular disease aged 60 to 79 years undergoing mammography screening at Kaiser Permanente Northern California between 10/24/2012 and 2/13/2015. Urine albumin-to-creatinine ratio (uACR), along with specific laboratory, demographic, and medical data, were measured at the baseline visit. Baseline estimated glomerular filtration rate (eGFR), medication history, and other comorbidities were identified from self-report and/or electronic medical records. BAC presence and gradation (mass) was measured by digital quantification of full-field mammograms.ResultsAmong 3,507 participants, 24.5% were aged ≥70 years, 63.5% were white, 7.5% had eGFR 0 mg) was 27.9%. Neither uACR ≥30 mg/g nor uACR ≥300 were significantly associated with BAC in crude or multivariable analyses. Reduced eGFR was associated with BAC in univariate analyses (odds ratio 1.53, 95% CI: 1.18-2.00), but the association was no longer significant after adjustment for potential confounders. Results were similar in various sensitivity analyses that used different BAC thresholds or analytic approaches.ConclusionsAmong women without cardiovascular disease undergoing mammography screening, reduced eGFR and albuminuria were not significantly associated with BAC