Racial Differences in Quantitative Measures of Area and Volumetric Breast Density.
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SUBMITTER: McCarthy AM
PROVIDER: S-EPMC5939658 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Mammographic breast density (MBD) is a strong independent risk factor for breast cancer (BC). We investigated the association between volumetric MBD measures, their changes over time, and BC risk in a cohort of women participating in the FEDRA (Florence-EPIC Digital mammographic density and breast cancer Risk Assessment) study. The study was carried out among 6148 women with repeated MBD measures from full-field digital mammograms and repeated information on lifestyle habits, reproductive history, and anthropometry. The association between MBD measures (modeled as time-dependent covariates), their relative annual changes, and BC risk were evaluated by adjusted Cox models. During an average of 7.8 years of follow-up, 262 BC cases were identified. BC risk was directly associated with standard deviation increments of volumetric percent density (VPD, HR 1.37, 95%CI 1.22-1.54) and dense volume (DV, HR 1.29, 95%CI 1.18-1.41). An inverse association emerged with non-dense volume (NDV, HR 0.82, 95%CI 0.69-0.98). No significant associations emerged between annual changes in VPD, DV, NDV, and BC risk. Higher values of MBD measures, modeled as time-dependent covariates, were positively associated with increased BC risk, while an inverse association was evident for increasing NDV. No effect of annual changes in MBD emerged.
| S-EPMC10046534 | biostudies-literature
Project description:BackgroundGiven that breast cancer and normal dense fibroglandular tissue have similar radiographic attenuation, we examine whether automated volumetric density measures identify a differential change between breasts in women with cancer and compare to healthy controls.MethodsEligible cases (n?=?1160) had unilateral invasive breast cancer and bilateral full-field digital mammograms (FFDMs) at two time points: within 2?months and 1-5?years before diagnosis. Controls (n?=?2360) were matched to cases on age and date of FFDMs. Dense volume (DV) and volumetric percent density (VPD) for each breast were assessed using Volpara™. Differences in DV and VPD between mammograms (median 3?years apart) were calculated per breast separately for cases and controls and their difference evaluated by using the Wilcoxon signed-rank test. To simulate clinical practice where cancer laterality is unknown, we examined whether the absolute difference between breasts can discriminate cases from controls using area under the ROC curve (AUC) analysis, adjusting for age, BMI, and time.ResultsAmong cases, the VPD and DV between mammograms of the cancerous breast decreased to a lesser degree (-?0.26% and -?2.10?cm3) than the normal breast (-?0.39% and -?2.74?cm3) for a difference of 0.13% (p value <?0.001) and 0.63?cm3 (p?=?0.002), respectively. Among controls, the differences between breasts were nearly identical for VPD (-?0.02 [p?=?0.92]) and DV (0.05 [p?=?0.77]). The AUC for discriminating cases from controls using absolute difference between breasts was 0.54 (95% CI 0.52, 0.56) for VPD and 0.56 (95% CI, 0.54, 0.58) for DV.ConclusionThere is a small relative increase in volumetric density measures over time in the breast with cancer which is not found in the normal breast. However, the magnitude of this difference is small, and this measure alone does not appear to be a good discriminator between women with and without breast cancer.
| S-EPMC6819393 | biostudies-literature
Project description:IntroductionMammographic density, the white radiolucent part of a mammogram, is a marker of breast cancer risk and mammographic sensitivity. There are several means of measuring mammographic density, among which are area-based and volumetric-based approaches. Current volumetric methods use only unprocessed, raw mammograms, which is a problematic restriction since such raw mammograms are normally not stored. We describe fully automated methods for measuring both area and volumetric mammographic density from processed images.MethodsThe data set used in this study comprises raw and processed images of the same view from 1462 women. We developed two algorithms for processed images, an automated area-based approach (CASAM-Area) and a volumetric-based approach (CASAM-Vol). The latter method was based on training a random forest prediction model with image statistical features as predictors, against a volumetric measure, Volpara, for corresponding raw images. We contrast the three methods, CASAM-Area, CASAM-Vol and Volpara directly and in terms of association with breast cancer risk and a known genetic variant for mammographic density and breast cancer, rs10995190 in the gene ZNF365. Associations with breast cancer risk were evaluated using images from 47 breast cancer cases and 1011 control subjects. The genetic association analysis was based on 1011 control subjects.ResultsAll three measures of mammographic density were associated with breast cancer risk and rs10995190 (p<0.025 for breast cancer risk and p<1 × 10(-6) for rs10995190). After adjusting for one of the measures there remained little or no evidence of residual association with the remaining density measures (p>0.10 for risk, p>0.03 for rs10995190).ConclusionsOur results show that it is possible to obtain reliable automated measures of volumetric and area mammographic density from processed digital images. Area and volumetric measures of density on processed digital images performed similar in terms of risk and genetic association.
| S-EPMC4203856 | biostudies-literature
Project description:Background While digital breast tomosynthesis (DBT) is rapidly replacing digital mammography (DM) in breast cancer screening, the potential of DBT density measures for breast cancer risk assessment remains largely unexplored. Purpose To compare associations of breast density estimates from DBT and DM with breast cancer. Materials and Methods This retrospective case-control study used contralateral DM/DBT studies from women with unilateral breast cancer and age- and ethnicity-matched controls (September 19, 2011-January 6, 2015). Volumetric percent density (VPD%) was estimated from DBT using previously validated software. For comparison, the publicly available Laboratory for Individualized Breast Radiodensity Assessment software package, or LIBRA, was used to estimate area-based percent density (APD%) from raw and processed DM images. The commercial Quantra and Volpara software packages were applied to raw DM images to estimate VPD% with use of physics-based models. Density measures were compared by using Spearman correlation coefficients (r), and conditional logistic regression was performed to examine density associations (odds ratios [OR]) with breast cancer, adjusting for age and body mass index. Results A total of 132 women diagnosed with breast cancer (mean age ± standard deviation [SD], 60 years ± 11) and 528 controls (mean age, 60 years ± 11) were included. Moderate correlations between DBT and DM density measures (r = 0.32-0.75; all P < .001) were observed. Volumetric density estimates calculated from DBT (OR, 2.3 [95% CI: 1.6, 3.4] per SD for VPD%DBT) were more strongly associated with breast cancer than DM-derived density for both APD% (OR, 1.3 [95% CI: 0.9, 1.9] [P < .001] and 1.7 [95% CI: 1.2, 2.3] [P = .004] per SD for LIBRA raw and processed data, respectively) and VPD% (OR, 1.6 [95% CI: 1.1, 2.4] [P = .01] and 1.7 [95% CI: 1.2, 2.6] [P = .04] per SD for Volpara and Quantra, respectively). Conclusion The associations between quantitative breast density estimates and breast cancer risk are stronger for digital breast tomosynthesis compared with digital mammography. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Yaffe in this issue.
| S-EPMC8608738 | biostudies-literature
Project description:The epidemiologic information regarding international differences in bone mineral density (BMD) in women is currently insufficient. We compared BMD in older women across five racial/ethnic groups in four countries. The femoral neck, total hip, and lumbar spine BMD were measured in women (aged 65-74 years) from the Study of Osteoporotic Fractures (SOF) (5,035 Caucasian women and 256 African American women in the US), the Tobago Women's Health Study (116 Afro-Caribbean women), the Ms Os Hong Kong Study (794 Hong Kong Chinese women) and the Namwon Study (1,377 South Korean women). BMD was corrected according to the cross-site calibration results for all scanners. When compared with US Caucasian women, the age adjusted mean BMD measurements at the hip sites were 21-31 % higher among Tobago Afro-Caribbean women and 13-23 % higher among African American women. The total hip and spine BMD values were 4-5 % lower among Hong Kong Chinese women and 4-7 % lower among South Korean women compared to US Caucasians. The femoral neck BMD was similar in Hong Kong Chinese women, but higher among South Korean women compared to US Caucasians. Current/past estrogen use was a significant contributing factor to the difference in BMD between US versus non-US women. Differences in body weight partially explained the difference in BMD between Asian versus non-Asian women. These findings show substantial racial/ethnic differences in BMD even within African or Asian origin individuals, and highlight the contributing role of body weight and estrogen use to the geographic and racial/ethnic variation in BMD.
| S-EPMC4109723 | biostudies-literature
Project description:PurposeTo assess a fully automated method for volumetric breast density (VBD) estimation in digital breast tomosynthesis (DBT) and to compare the findings with those of full-field digital mammography (FFDM) and magnetic resonance (MR) imaging.Materials and methodsBilateral DBT images, FFDM images, and sagittal breast MR images were retrospectively collected from 68 women who underwent breast cancer screening from October 2011 to September 2012 with institutional review board-approved, HIPAA-compliant protocols. A fully automated computer algorithm was developed for quantitative estimation of VBD from DBT images. FFDM images were processed with U.S. Food and Drug Administration-cleared software, and the MR images were processed with a previously validated automated algorithm to obtain corresponding VBD estimates. Pearson correlation and analysis of variance with Tukey-Kramer post hoc correction were used to compare the multimodality VBD estimates.ResultsEstimates of VBD from DBT were significantly correlated with FFDM-based and MR imaging-based estimates with r = 0.83 (95% confidence interval [CI]: 0.74, 0.90) and r = 0.88 (95% CI: 0.82, 0.93), respectively (P < .001). The corresponding correlation between FFDM and MR imaging was r = 0.84 (95% CI: 0.76, 0.90). However, statistically significant differences after post hoc correction (α = 0.05) were found among VBD estimates from FFDM (mean ± standard deviation, 11.1% ± 7.0) relative to MR imaging (16.6% ± 11.2) and DBT (19.8% ± 16.2). Differences between VDB estimates from DBT and MR imaging were not significant (P = .26).ConclusionFully automated VBD estimates from DBT, FFDM, and MR imaging are strongly correlated but show statistically significant differences. Therefore, absolute differences in VBD between FFDM, DBT, and MR imaging should be considered in breast cancer risk assessment.
| S-EPMC4819897 | biostudies-literature
Project description:BackgroundUnderstanding how changes in body mass index (BMI) relate to changes in mammographic density is necessary to evaluate adjustment for BMI gain/loss in studies of change in density and breast cancer risk. Increase in BMI has been associated with a decrease in percent density, but the effect on change in absolute dense area or volume is unclear.MethodsWe examined the association between change in BMI and change in volumetric breast density among 24,556 women in the San Francisco Mammography Registry from 2007 to 2013. Height and weight were self-reported at the time of mammography. Breast density was assessed using single x-ray absorptiometry measurements. Cross-sectional and longitudinal associations between BMI and dense volume (DV), non-dense volume (NDV), and percent dense volume (PDV) were assessed using multivariable linear regression models, adjusted for demographics, risk factors, and reproductive history.ResultsIn cross-sectional analysis, BMI was positively associated with DV [β, 2.95 cm(3); 95% confidence interval (CI), 2.69-3.21] and inversely associated with PDV (β, -2.03%; 95% CI, -2.09, -1.98). In contrast, increasing BMI was longitudinally associated with a decrease in both DV (β, -1.01 cm(3); 95% CI, -1.59, -0.42) and PDV (β, -1.17%; 95% CI, -1.31, -1.04). These findings were consistent for both pre- and postmenopausal women.ConclusionOur findings support an inverse association between change in BMI and change in PDV. The association between increasing BMI and decreasing DV requires confirmation.ImpactLongitudinal studies of PDV and breast cancer risk, or those using PDV as an indicator of breast cancer risk, should evaluate adjustment for change in BMI.
| S-EPMC4633314 | biostudies-literature
Project description:ObjectiveThe study objective was to examine the impact of race/ethnicity on associations between anthropometric measures and diabetes risk.Research design and methodsA total of 136,112 postmenopausal women aged 50-79 years participating in the Women's Health Initiative without baseline cancer or diabetes were followed for 14.6 years. BMI, waist circumference (WC), and waist-to-hip ratio (WHR) were measured in all participants, and a subset of 9,695 had assessment of whole-body fat mass, whole-body percent fat, trunk fat mass, and leg fat mass by DXA. Incident diabetes was assessed via self-report. Multivariate Cox proportional hazards regression models were used to assess associations between anthropometrics and diabetes incidence.ResultsDuring follow-up, 18,706 cases of incident diabetes were identified. BMI, WC, and WHR were all positively associated with diabetes risk in each racial and ethnic group. WC had the strongest association with risk of diabetes across all racial and ethnic groups. Compared with non-Hispanic whites, associations with WC were weaker in black women (P < 0.0001) and stronger in Asian women (P < 0.0001). Among women with DXA determinations, black women had a weaker association with whole-body fat (P = 0.02) but a stronger association with trunk-to-leg fat ratio (P = 0.03) compared with white women.ConclusionsIn postmenopausal women across all racial/ethnic groups, WC was a better predictor of diabetes risk, especially for Asian women. Better anthropometric measures that reflect trunk-to-leg fat ratio may improve diabetes risk assessment for black women.
| S-EPMC6463546 | biostudies-literature
Project description:Background: Reductions in breast density with tamoxifen and aromatase inhibitors may be an intermediate marker of treatment response. We compare changes in volumetric breast density among breast cancer cases using tamoxifen or aromatase inhibitors (AI) to untreated women without breast cancer.Methods: Breast cancer cases with a digital mammogram prior to diagnosis and after initiation of tamoxifen (n = 366) or AI (n = 403) and a sample of controls (n = 2170) were identified from the Mayo Clinic Mammography Practice and San Francisco Mammography Registry. Volumetric percent density (VPD) and dense breast volume (DV) were measured using Volpara (Matakina Technology) and Quantra (Hologic) software. Linear regression estimated the effect of treatment on annualized changes in density.Results: Premenopausal women using tamoxifen experienced annualized declines in VPD of 1.17% to 1.70% compared with 0.30% to 0.56% for controls and declines in DV of 7.43 to 15.13 cm3 compared with 0.28 to 0.63 cm3 in controls, for Volpara and Quantra, respectively. The greatest reductions were observed among women with ≥10% baseline density. Postmenopausal AI users had greater declines in VPD than controls (Volpara P = 0.02; Quantra P = 0.03), and reductions were greatest among women with ≥10% baseline density. Declines in VPD among postmenopausal women using tamoxifen were only statistically greater than controls when measured with Quantra.Conclusions: Automated software can detect volumetric breast density changes among women on tamoxifen and AI.Impact: If declines in volumetric density predict breast cancer outcomes, these measures may be used as interim prognostic indicators. Cancer Epidemiol Biomarkers Prev; 26(6); 930-7. ©2017 AACR.
| S-EPMC5457346 | biostudies-literature
Project description:BackgroundHigher mammographic density (MD), a radiological measure of the proportion of fibroglandular tissue in the breast, and lower terminal duct lobular unit (TDLU) involution, a histological measure of the amount of epithelial tissue in the breast, are independent breast cancer risk factors. Previous studies among predominantly white women have associated reduced TDLU involution with higher MD.MethodsIn this cohort of 611 invasive breast cancer patients (ages 23-91 years [58.4% ≥ 50 years]) from China, where breast cancer incidence rates are lower and the prevalence of dense breasts is higher compared with Western countries, we examined the associations between TDLU involution assessed in tumor-adjacent normal breast tissue and quantitative MD assessed in the contralateral breast obtained from the VolparaDensity software. Associations were estimated using generalized linear models with MD measures as the outcome variables (log-transformed), TDLU measures as explanatory variables (categorized into quartiles or tertiles), and adjusted for age, body mass index, parity, age at menarche and breast cancer subtype.ResultsWe found that, among all women, percent dense volume (PDV) was positively associated with TDLU count (highest tertile vs. zero: Expbeta = 1.28, 95% confidence interval [CI] 1.08-1.51, ptrend = < .0001), TDLU span (highest vs. lowest tertile: Expbeta = 1.23, 95% CI 1.11-1.37, ptrend = < .0001) and acini count/TDLU (highest vs. lowest tertile: Expbeta = 1.22, 95% CI 1.09-1.37, ptrend = 0.0005), while non-dense volume (NDV) was inversely associated with these measures. Similar trend was observed for absolute dense volume (ADV) after the adjustment of total breast volume, although the associations for ADV were in general weaker than those for PDV. The MD-TDLU associations were generally more pronounced among breast cancer patients ≥ 50 years and those with luminal A tumors compared with patients < 50 years and with luminal B tumors.ConclusionsOur findings based on quantitative MD and TDLU involution measures among Chinese breast cancer patients are largely consistent with those reported in Western populations and may provide additional insights into the complexity of the relationship, which varies by age, and possibly breast cancer subtype.
| S-EPMC11247848 | biostudies-literature