Percent Mammographic Density Research Articles

Residential particulate matter and distance to roadways in relation to mammographic density: results from the Nurses\u2019 Health Studies

BackgroundHigh mammographic density is a strong, well-established breast cancer risk factor. Three studies conducted in various smaller geographic settings reported inconsistent findings between air pollution and mammographic density. We assessed whether particulate matter (PM) exposures (PM2.5, PM2.5–10, and PM10) and distance to roadways were associated with mammographic density among women residing across the United States.MethodsThe Nurses’ Health Studies are prospective cohorts for whom a subset has screening mammograms from the 1990s (interquartile range 1990–1999). PM was estimated using spatio-temporal models linked to residential addresses. Among 3258 women (average age at mammogram 52.7 years), we performed multivariable linear regression to assess associations between square-root-transformed mammographic density and PM within 1 and 3 years before the mammogram. For linear regression estimates of PM in relation to untransformed mammographic density outcomes, bootstrapped robust standard errors are used to calculate 95% confidence intervals (CIs). Analyses were stratified by menopausal status and region of residence.ResultsRecent PM and distance to roadways were not associated with mammographic density in premenopausal women (PM2.5 within 3 years before mammogram β = 0.05, 95% CI –0.16, 0.27; PM2.5–10 β = 0, 95%, CI –0.15, 0.16; PM10 β = 0.02, 95% CI –0.10, 0.13) and postmenopausal women (PM2.5 within 3 years before mammogram β = –0.05, 95% CI –0.27, 0.17; PM2.5–10 β = –0.01, 95% CI –0.16, 0.14; PM10 β = –0.02, 95% CI –0.13, 0.09). Largely null associations were observed within regions. Suggestive associations were observed among postmenopausal women in the Northeast (n = 745), where a 10-μg/m3 increase in PM2.5 within 3 years before the mammogram was associated with 3.4 percentage points higher percent mammographic density (95% CI –0.5, 7.3).ConclusionsThese findings do not support that recent PM or roadway exposures influence mammographic density. Although PM was largely not associated with mammographic density, we cannot rule out the role of PM during earlier exposure time windows and possible associations among northeastern postmenopausal women.

Growth Trajectories, Breast Size, and Breast-Tissue Composition in a British Prebirth Cohort of Young Women.

Mammographic percent density, the proportion of fibroglandular tissue in the breast, is a strong risk factor for breast cancer, but its determinants in young women are unknown. We examined associations of magnetic resonance imaging (MRI) breast-tissue composition at age 21 years with prospectively collected measurements of body size and composition from birth to early adulthood and markers of puberty (all standardized) in a sample of 500 nulliparous women from a prebirth cohort of children born in Avon, United Kingdom, in 1991-1992 and followed up to 2011-2014. Linear models were fitted to estimate relative change in MRI percent water, which is equivalent to mammographic percent density, associated with a 1-standard-deviation increase in the exposure of interest. In mutually adjusted analyses, MRI percent water was positively associated with birth weight (relative change (RC) = 1.03, 95% confidence interval (CI): 1.00, 1.06) and pubertal height growth (RC = 1.07, 95% CI: 1.02, 1.13) but inversely associated with pubertal weight growth (RC = 0.86, 95% CI: 0.84, 0.89) and changes in dual-energy x-ray absorptiometry percent body fat mass (e.g., for change between ages 11 years and 13.5 years, RC = 0.96, 95% CI: 0.93, 0.99). Ages at thelarche and menarche were positively associated with MRI percent water, but these associations did not persist upon adjustment for height and weight growth. These findings support the hypothesis that growth trajectories influence breast-tissue composition in young women, whereas puberty plays no independent role.

Occupational exposures and mammographic density in Spanish women

ObjectivesThe association between occupational exposures and mammographic density (MD), a marker of breast cancer risk, has not been previously explored. Our objective was to investigate the influence of occupational exposure...

Breast density scales: the metric matters.

Measures of percent mammographic density (PMD) are often categorized using various density scales. The purpose of this study was to examine information loss associated with the use of categorical density scales. Baseline PMD was assessed at 1% precision for 2,374 females. The data were used to create 21-category, 4-category and 2-category density scales. R-squared and root mean square error were used to evaluate the effect of categorizing PMD. The area under the receiver operator characteristic curves were compared between cancer risk models employing solely categorical PMD scales and solely baseline PMD for a subset of females (424 cases, 848 controls). R-squared value decreased from 1.00 (1% PMD) to 0.56 (2-category scale), while root mean square error increased from 0.00 (1% PMD) to 10.83 (2-category scale). The area under the receiver operator characteristic curve decreased from 0.64 for a cancer risk model using 1% PMD to 0.58 for a risk model using a 21-category density scale (p < 0.0001), 0.55 for a 4-category Breast Imaging, Reporting and Data System-like scale (p < 0.0001) and 0.50 for a 2-category Breast Imaging, Reporting and Data System-like scale (high vs low) (p < 0.0001). Categorizing PMD measures into categorical density scales leads to a significant loss of information. Indeed, a simple high versus low split of PMD using a 50% cut point yields a cancer risk model with no discriminatory power. Advances in knowledge: Use of categorical mammographic density scales rather than continuous percent mammographic density measures leads to significant loss of information. Breast cancer risk models using categorical mammographic density scales perform more poorly than models using continuous PMD measures.

Using automated texture features to determine the probability for masking of a tumor on mammography, but not ultrasound

BackgroundTumors in radiologically dense breast were overlooked on mammograms more often than tumors in low-density breasts. A fast reproducible and automated method of assessing percentage mammographic density (PMD) would be desirable to support decisions whether ultrasonography should be provided for women in addition to mammography in diagnostic mammography units. PMD assessment has still not been included in clinical routine work, as there are issues of interobserver variability and the procedure is quite time consuming. This study investigated whether fully automatically generated texture features of mammograms can replace time-consuming semi-automatic PMD assessment to predict a patient’s risk of having an invasive breast tumor that is visible on ultrasound but masked on mammography (mammography failure).MethodsThis observational study included 1334 women with invasive breast cancer treated at a hospital-based diagnostic mammography unit. Ultrasound was available for the entire cohort as part of routine diagnosis. Computer-based threshold PMD assessments (“observed PMD”) were carried out and 363 texture features were obtained from each mammogram. Several variable selection and regression techniques (univariate selection, lasso, boosting, random forest) were applied to predict PMD from the texture features. The predicted PMD values were each used as new predictor for masking in logistic regression models together with clinical predictors. These four logistic regression models with predicted PMD were compared among themselves and with a logistic regression model with observed PMD. The most accurate masking prediction was determined by cross-validation.ResultsAbout 120 of the 363 texture features were selected for predicting PMD. Density predictions with boosting were the best substitute for observed PMD to predict masking. Overall, the corresponding logistic regression model performed better (cross-validated AUC, 0.747) than one without mammographic density (0.734), but less well than the one with the observed PMD (0.753). However, in patients with an assigned mammography failure risk >10%, covering about half of all masked tumors, the boosting-based model performed at least as accurately as the original PMD model.ConclusionAutomatically generated texture features can replace semi-automatically determined PMD in a prediction model for mammography failure, such that more than 50% of masked tumors could be discovered.

Association between mammographic density and pregnancies relative to age and BMI: a breast cancer case-only analysis.

Percentage mammographic density (PMD) is a major risk factor for breast cancer (BC). It is strongly associated with body mass index (BMI) and age, which are themselves risk factors for breast cancer. This analysis investigated the association between the number of full-term pregnancies and PMD in different subgroups relative to age and BMI. Patients were identified in the breast cancer database of the University Breast Center for Franconia. A total of 2410 patients were identified, for whom information on parity, age, and BMI, and a mammogram from the time of first diagnosis were available for assessing PMD. Linear regression analyses were conducted to investigate the influence on PMD of the number of full-term pregnancies (FTPs), age, BMI, and interaction terms between them. As in previous studies, age, number of FTPs, and BMI were found to be associated with PMD in the expected direction. However, including the respective interaction terms improved the prediction of PMD even further. Specifically, the association between PMD and the number of FTPs differed in young patients under the age of 45 (mean decrease of 0.37 PMD units per pregnancy) from the association in older age groups (mean decrease between 2.29 and 2.39 PMD units). BMI did not alter the association between PMD and the number of FTPs. The effect of pregnancies on mammographic density does not appear to become apparent before the age of menopause. The mechanism that drives the effect of pregnancies on mammographic density appears to be counter-regulated by other influences on mammographic density in younger patients.

Optical assessment of mammographic breast density by a 12-wavelength vs a continuous-spectrum optical spectroscopy device.

A 12-laser-wavelength, fixed source-detector position, cup-based optical breast spectroscopy (OBS) device was developed for use in large-scale, multicenter trials as a mammographic breast density (MBD) quantification and breast cancer (BC) risk prescreening tool. In this study, the device was evaluated in comparison with a spectrometer-based device used in previous studies. The devices were compared on their ability to predict mammographic percent density (MPD) and to identify women with high MBD from optical spectra. OBS measurements were made on 60 women, (age 29-73), using both devices. Recent mammograms were collected for all women and MPD quantified from the mammograms. Principal components (PCs) analysis was performed on both sets of OBS spectra, and multivariate logistic regression analysis of the resulting PC scores was used to identify women with high MBD. Both devices are able to identify high MBD with very high sensitivity and specificity. Partial least-squares regression of the spectra was used to predict MPD. Both devices show a strong correlation between OBS-predicted MPD and MPD read from mammograms, however, the correlation is stronger for the continuous-spectrum device (r = 0.74, P = .001) than for the 12-wavelength device (r = 0.62, P = .004). Improvements to the cup-based device to reduce detector saturation should improve the prediction of MPD from the spectra.

A Randomized Double-Blind Placebo-Controlled Trial of the Effect of Vitamin D3 Supplementation on Breast Density in Premenopausal Women.

Background: This double-blind, placebo-controlled parallel group trial assessed whether oral supplementation with 1,000, 2,000, or 3,000 IU/day vitamin D3 over one year reduces percent mammographic breast density in premenopausal women.Methods: The trial was conducted between October 2012 and June 2015, among premenopausal female volunteers from Quebec City (Quebec, Canada). Women were randomized with ratio 1:1:1:1 to one of four study arms (1,000, 2,000, or 3,000 IU/day vitamin D3 or placebo). The primary outcome was mean change in percent mammographic breast density. Participants and research team were blinded to study arm assignment.Results: Participants (n = 405) were randomized to receive 1,000 (n = 101), 2,000 (n = 104), or 3,000 IU/day (n = 101) vitamin D3, or a placebo (n = 99). The primary analysis included 391 participants (96, 99, 100, and 96, respectively). After the one-year intervention, mean ± SE change in percent breast density in the arms 1,000 IU/day (-5.5% ± 0.5%) and 2,000 IU/day (-5.9% ± 0.5%) vitamin D3 was similar to that in the placebo arm (-5.7% ± 0.5%) (P values = 1.0). In the 3,000 IU/day vitamin D3 arm, percent breast density also declined but slightly less (-3.8% ± 0.5%) compared with placebo arm (P = 0.03). Adherence to intervention was excellent (92.8%), and reporting of health problems was comparable among study arms (P ≥ 0.95). All participants had normal serum calcium.Conclusions: In premenopausal women, one-year supplementation with 1,000, 2,000, or 3,000 IU/day vitamin D3 resulted in a reduction of percent breast density no greater than that seen with the placebo.Impact: At doses of 1,000-3,000 IU/day, vitamin D supplementation will not reduce breast cancer risk through changes in breast density. Cancer Epidemiol Biomarkers Prev; 26(8); 1233-41. ©2017 AACR.

Mammographic density, blood telomere length and lipid peroxidation

Extensive mammographic density is a strong risk factor for breast cancer, but may also be an indicator of biological age. In this study we examined whether mammographic density is related to blood telomere length, a potential marker of susceptibility to age-related disease. We measured mammographic density by a computer assisted method and blood telomere length using a validated PCR method. Urinary malondialdehyde (MDA), a marker of lipid peroxidation, was measured in 24 hour urine collections. In the 342 women examined telomere length was negatively correlated with age, was lower in postmenopausal compared to premenopausal women and in smokers compared to non-smokers, and was positively correlated with urinary MDA. Telomere length was not associated with percent mammographic density or dense area, before or after adjustment for risk factors and MDA. However, there was a significant interaction between telomere length and MDA in their association with mammographic density. At lower levels of MDA, mammographic density and telomere length were inversely associated; while at high levels of MDA, there was evidence of a J-shaped association between mammographic density and telomere length. Further work is need to replicate these results and to examine the association of mammographic density with age-related chronic disease and mortality.

Breast cancer risk prediction: an update to the Rosner-Colditz breast cancer incidence model.

To update and expand the Rosner-Colditz breast cancer incidence model by evaluating the contributions of more recently identified risk factors as well as predicted percent mammographic density (MD) to breast cancer risk. Using data from the Nurses' Health Study (NHS) and NHSII, we added adolescent somatotype (9 unit scale), vegetable intake (servings/day), breastfeeding (months), physical activity (MET-h/week), and predicted percent MD to the Rosner-Colditz model to determine whether these variables improved model discrimination. We evaluated all invasive as well as ER+/PR+, ER+/PR-, and ER-/PR- breast cancer. In the NHS/NHSII, we accrued over 5200 cases of invasive breast cancer over more than 20years of follow-up with complete data on the risk factors. Adolescent somatotype and predicted percent MD significantly improved the original Rosner-Colditz model for all invasive breast cancer (change in age-adjusted AUC=0.020, p<0.001). The relative risk (RR) of invasive breast cancer for a 4-unit increase in adolescent somatotype was 0.62 (95% CI 0.56, 0.70), whereas the RR for a 20-unit increase in predicted percent MD was 1.32 (95% CI 1.28, 1.36). Adolescent somatotype and predicted percent MD also significantly improved the ER+/PR+model (change in age-adjusted AUC=0.020, p<0.001) as well as the ER+/PR- model (change in age-adjusted AUC=0.012, p=0.007). Adolescent somatotype, predicted percent MD, breastfeeding, and vegetable intake improved the ER-/PR- model (change in AUC=0.031, p<0.0001). The RR of ER-/PR- disease for 5 vegetable servings/day increase was 0.83 (95% CI 0.70, 0.99), while the RR for every 12months of breastfeeding was 0.88 (95% CI 0.77, 1.01). Physical activity did not improve risk classification in any model. Adolescent somatotype and predicted percent MD significantly improved breast cancer risk classification using the Rosner-Colditz model. Further, risk factors specific to ER- disease, such as breastfeeding and vegetable intake, may also help improve risk prediction of this aggressive subtype.

Tumor characteristics and family history in relation to mammographic density and breast cancer: The French E3N cohort

BackgroundMammographic density is a known heritable risk factor for breast cancer, but reports how tumor characteristics and family history may modify this association are inconsistent. MethodsDense and total breast areas were assessed using Cumulus™ from pre-diagnostic mammograms for 820 invasive breast cancer cases and 820 matched controls nested within the French E3N cohort study. To allow comparisons across models, percent mammographic density (PMD) was standardized to the distribution of the controls. Odds ratios (OR) and 95% confidence intervals (CI) of breast cancer risk for mammographic density were estimated by conditional logistic regression while adjusting for age and body mass index. Heterogeneity according to tumor characteristic and family history was assessed using stratified analyses. ResultsOverall, the OR per 1 SD for PMD was 1.50 (95% CI, 1.33–1.69). No evidence for significant heterogeneity by tumor size, lymph node status, grade, and hormone receptor status (estrogen, progesterone, and HER2) was detected. However, the association of PMD was stronger for women reporting a family history of breast cancer (OR1SD=2.25; 95% CI, 1.67–3.04) than in women reporting none (OR1SD=1.41; 95% CI, 1.24–1.60; pheterogeneity=0.002). Similarly, effect modification by FHBC was observed using categories of PMD (pheterogeneity=0.02) with respective ORs of 15.16 (95% CI, 4.23–54.28) vs. 3.14 (95% CI, 1.89–5.22) for ≥50% vs. <10% PMD. ConclusionsThe stronger association between mammographic density and breast cancer risk with a family history supports the hypothesis of shared genetic factors responsible for familial aggregation of breast cancer and the heritable component of mammographic density.

Circulating leptin and adiponectin, and breast density in premenopausal Mexican women: the Mexican Teachers' Cohort.

Leptin and adiponectin are produced by the adipose tissue. Mammographic density (MD) is one of the strongest predictors of breast cancer (BC) and is highly influenced by adiposity. How the interplay between MD, obesity, and obesity-related biomarkers influences BC risk, however, is still unknown, especially in premenopausal women, where adiposity seems to be protective for BC. The aim of the present study was to explore the association between circulating leptin, adiponectin, and their ratio, with MD in Mexican premenopausal women who are part of the large Mexican Teachers' Cohort (MTC). A subsample of 2,084 women from the MTC participated in a clinical evaluation. Of them, 574 premenopausal women were randomly selected, from four MD strata. Serum leptin and adiponectin concentrations were measured by immunoassays. Multivariate regression analyses were performed to compare means of MD by quartiles of adipokines and their ratio. High leptin and leptin/adiponectin ratio levels were significantly associated with lower percentage MD and higher absolute and non-absolute dense tissue areas. High adiponectin levels were significantly associated with lower absolute dense and non-dense tissue areas, but not with percentage MD. After adjustment for BMI, only the associations between percentage MD and absolute non-dense tissue area with leptin remained statistically significant. Leptin, adiponectin, and their ratio were associated with MD; however, only the positive association with leptin seemed to be independent from overall obesity.

Abstract 4235: Application of convolutional neural networks to breast biopsies to uncover tissue correlates of mammographic breast density

Abstract Background: High percent mammographic density (MD), which reflects the relative fibroglandular tissue content of the breast, is one of the strongest breast cancer risk factors; however, the pathologic mediators of this risk are unknown. We hypothesize that analysis of breast tissue sections using deep learning approaches may characterize histologic features that underpin risk associated with high MD. Methods: Non-targeted H&amp;E stained breast tissue sections of diagnostic image-guided breast biopsies were evaluated among 588 women enrolled following an abnormal mammogram in the Breast Radiology Evaluation and Study of Tissues (BREAST) Stamp Project (2007-2010). Overall volumetric percent MD for the biopsied breast and localized volumetric percent MD surrounding the biopsy site were determined for each participant. A deep convolutional neural network (CNN) model was trained to identify and quantitatively assess breast epithelial, stroma and fat tissue and their organizational and spatial arrangements. Least absolute shrinkage and selection operator (Lasso) regression was used to determine relationships between MD measures and pathological features. To ensure reliability of the model, a cross-validation strategy was employed to build and assess the performance of the fitted model. Finally, Spearman correlation coefficients were estimated to test the association between the predicted density values by each model (predicting overall or localized MD) and the actual MD measurements. We report the average and standard deviation (SD) of the correlation coefficients. Results: In an independent validation set, the CNN model was 95.5% accurate in classifying epithelial, stromal and fat tissue. The mean (SD) correlations between the predicted model and the actual measurements for overall and localized MD were 0.70 (0.06) and 0.65 (0.06) respectively. The amount of stroma identified (normalized to tissue area) had the highest selection probability (P-value) by the Lasso model and thus the strongest positive relationship with MD (P-value &amp;gt;0.9 for each MD measurement). In contrast, the amount of normalized epithelial tissue was not related to MD (P-value=0.01 for each MD measurement). No association was observed for the total normalized fat area with MD (P-value &amp;lt;0.31 for each MD measurement). In addition, the number of distributed epithelial regions was positively associated, whereas the distance between epithelial regions was inversely associated with overall MD (P-value &amp;lt;0.87 and 0.62, respectively). Conclusions: These results show that greater stromal tissue amount and spatial distribution patterns of epithelial regions, rather than total epithelial amounts, had the strongest relationships with elevated MD. Future work will determine the relationship of these MD features with biopsy diagnosis. Citation Format: Maeve Mullooly, Babak Ehteshami Bejnordi, Maya Palakal, Pamela M. Vacek, Donald L. Weaver, John A. Shepherd, Bo Fan, Amir Pasha Mahmoudzadeh, Jeff Wang, Jason M. Johnson, Sally D. Herschorn, Brian L. Sprague, Ruth M. Pfeiffer, Louise A. Brinton, Mark E. Sherman, Andrew Beck, Gretchen L. Gierach. Application of convolutional neural networks to breast biopsies to uncover tissue correlates of mammographic breast density [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4235. doi:10.1158/1538-7445.AM2017-4235

Mammographic density and ageing: A collaborative pooled analysis of cross-sectional data from 22 countries worldwide.

BackgroundMammographic density (MD) is one of the strongest breast cancer risk factors. Its age-related characteristics have been studied in women in western countries, but whether these associations apply to women worldwide is not known.Methods and findingsWe examined cross-sectional differences in MD by age and menopausal status in over 11,000 breast-cancer-free women aged 35–85 years, from 40 ethnicity- and location-specific population groups across 22 countries in the International Consortium on Mammographic Density (ICMD). MD was read centrally using a quantitative method (Cumulus) and its square-root metrics were analysed using meta-analysis of group-level estimates and linear regression models of pooled data, adjusted for body mass index, reproductive factors, mammogram view, image type, and reader. In all, 4,534 women were premenopausal, and 6,481 postmenopausal, at the time of mammography. A large age-adjusted difference in percent MD (PD) between post- and premenopausal women was apparent (–0.46 cm [95% CI: −0.53, −0.39]) and appeared greater in women with lower breast cancer risk profiles; variation across population groups due to heterogeneity (I2) was 16.5%. Among premenopausal women, the √PD difference per 10-year increase in age was −0.24 cm (95% CI: −0.34, −0.14; I2 = 30%), reflecting a compositional change (lower dense area and higher non-dense area, with no difference in breast area). In postmenopausal women, the corresponding difference in √PD (−0.38 cm [95% CI: −0.44, −0.33]; I2 = 30%) was additionally driven by increasing breast area. The study is limited by different mammography systems and its cross-sectional rather than longitudinal nature.ConclusionsDeclines in MD with increasing age are present premenopausally, continue postmenopausally, and are most pronounced over the menopausal transition. These effects were highly consistent across diverse groups of women worldwide, suggesting that they result from an intrinsic biological, likely hormonal, mechanism common to women. If cumulative breast density is a key determinant of breast cancer risk, younger ages may be the more critical periods for lifestyle modifications aimed at breast density and breast cancer risk reduction.

High Mammographic Density in Long-Term Night-Shift Workers: DDM-Spain/Var-DDM.

Background: Night-shift work (NSW) has been suggested as a possible cause of breast cancer, and its association with mammographic density (MD), one of the strongest risk factors for breast cancer, has been scarcely addressed. This study examined NSW and MD in Spanish women.Methods: The study covered 2,752 women aged 45-68 years recruited in 2007-2008 in 7 population-based public breast cancer screening centers, which included 243 women who had performed NSW for at least one year. Occupational data and information on potential confounders were collected by personal interview. Two trained radiologist estimated the percentage of MD assisted by a validated semiautomatic computer tool (DM-scan). Multivariable mixed linear regression models with random screening center-specific intercepts were fitted using log-transformed percentage of MD as the dependent variable and adjusting by known confounding variables.Results: Having ever worked in NSW was not associated with MD [Formula: see text]:0.96; 95% confidence interval (CI), 0.86-1.06]. However, the adjusted geometric mean of the percentage of MD in women with NSW for more than 15 years was 25% higher than that of those without NSW history (MD>15 years:20.7% vs. MDnever:16.5%;[Formula: see text]:1.25; 95% CI,1.01-1.54). This association was mainly observed in postmenopausal participants ([Formula: see text]:1.28; 95% CI, 1.00-1.64). Among NSW-exposed women, those with ≤2 night-shifts per week had higher MD than those with 5 to 7 nightshifts per week ([Formula: see text]:1.42; 95% CI, 1.10-1.84).Conclusions: Performing NSW was associated with higher MD only in women with more than 15 years of cumulated exposure. These findings warrant replication in futures studies.Impact: Our findings suggest that MD could play a role in the pathway between long-term NSW and breast cancer. Cancer Epidemiol Biomarkers Prev; 26(6); 905-13. ©2017 AACR.

Risk stratification of women with false-positive test results in mammography screening based on mammographic morphology and density: A case control study

BackgroundThe long-term risk of breast cancer is increased in women with false-positive (FP) mammography screening results. We investigated whether mammographic morphology and/or density can be used to stratify these women according to their risk of future breast cancer MethodsWe undertook a case-control study nested in the population-based screening programme in Copenhagen, Denmark. We included 288 cases and 288 controls based on a cohort of 4743 women with at least one FP-test result in 1991–2005 who were followed up until 17 April 2008. Film-based mammograms were assessed using the Breast Imaging-Reporting and Data System (BI-RADS) density classification, the Tabár classification, and two automated techniques quantifying percentage mammographic density (PMD) and mammographic texture (MTR), respectively. The association with breast cancer was estimated using binary logistic regression calculating Odds Ratios (ORs) and the area under the receiver operating characteristic (ROC) curves (AUCs) adjusted for birth year and age and invitation round at the FP-screen ResultsSignificantly increased ORs were seen for BI-RADS D(density)2-D4 (OR 1.94; 1.30-2.91, 2.36; 1.51-3.70 and 4.01; 1.67-9.62, respectively), Tabár’s P(pattern)IV (OR 1.83; 1.16-2.89), PMD Q(quartile)2-Q4 (OR 1.71; 1.02-2.88, 1.97; 1.16-3.35 and 2.43; 1.41-4.19, respectively) and MTR Q4 (1.97; 1.12-3.46) using the lowest/fattiest category as reference ConclusionAll four methods, capturing either mammographic morphology or density, could segregate women with FP-screening results according to their risk of future breast cancer − using already available screening mammograms. Our findings need validation on digital mammograms, but may inform potential future risk stratification and tailored screening strategies

Percent mammographic density prediction: development of a model in the nurses' health studies.

To develop a model to predict percent mammographic density (MD) using questionnaire data and mammograms from controls in the Nurses' Health Studies' nested breast cancer case-control studies. Further, we assessed the association between both measured and predicted percent MD and breast cancer risk. Using data from 2,955 controls, we assessed several variables as potential predictors. We randomly divided our dataset into a training dataset (two-thirds of the dataset) and a testing dataset (one-third of the dataset). We used stepwise linear regression to identify the subset of variables that were most predictive. Next, we examined the correlation between measured and predicted percent MD in the testing dataset and computed the r 2 in the total dataset. We used logistic regression to examine the association between measured and predicted percent MD and breast cancer risk. In the training dataset, several variables were selected for inclusion, including age, body mass index, and parity, among others. In the testing dataset, the Spearman correlation coefficient between predicted and measured percent MD was 0.61. As the prediction model performed well in the testing dataset, we developed the final model in the total dataset. The final prediction model explained 41% of the variability in percent MD. Both measured and predicted percent MD were similarly associated with breast cancer risk adjusting for age, menopausal status, and hormone use (OR per five unit increase=1.09 for both). These results suggest that predicted percent MD may be useful for research studies in which mammograms are unavailable.

Correlation of mammographic density and serum calcium levels in patients with primary breast cancer.

Percentage mammographic breast density (PMD) is one of the most important risk factors for breast cancer (BC). Calcium, vitamin D, bisphosphonates, and denosumab have been considered and partly confirmed as factors potentially influencing the risk of BC. This retrospective observational study investigated the association between serum calcium level and PMD. A total of 982 BC patients identified in the research database at the University Breast Center for Franconia with unilateral BC, calcium and albumin values, and mammogram at the time of first diagnosis were included. PMD was assessed, using a semiautomated method by two readers. Linear regression analyses were conducted to investigate the impact on PMD of the parameters of serum calcium level adjusted for albumin level, and well‐known clinical predictors such as age, body mass index (BMI), menopausal status and confounder for serum calcium like season in which the BC was diagnosed. Increased calcium levels were associated with reduced PMD (P = 0.024). Furthermore, PMD was inversely associated with BMI (P < 0.001) and age (P < 0.001). There was also an association between PMD and menopausal status (P < 0.001). The goodness‐of‐fit of the regression model was moderate. This is the first study assessing the association between serum calcium level and PMD. An inverse association with adjusted serum calcium levels was observed. These findings add to previously published data relating to vitamin D, bisphosphonates, denosumab, and the RANK/RANKL signaling pathway in breast cancer risk and prevention.

Association between expression of inflammatory markers in normal breast tissue and mammographic density among premenopausal and postmenopausal women.

Inflammatory markers may be associated with breast cancer risk. We assessed the association between expression levels of proinflammatory (interleukin 6, tumor necrosis factor-α, C-reactive protein, cyclooxygenase 2, leptin, serum amyloid A1, interleukin 8, and signal transducer and activator of transcription 3) and anti-inflammatory markers (transforming growth factor-β, interleukin 10, and lactoferrin) in normal breast tissue with mammographic density, a strong breast cancer risk indicator, among 163 breast cancer patients. The expression of inflammatory markers was visually evaluated on immunohistochemistry stained slides. The percent mammographic density (PMD) was estimated by a computer-assisted method in the contralateral cancer-free breast. We used generalized linear models to estimate means of PMD by median expression levels of the inflammatory markers while adjusting for age and waist circumference. Higher expression levels (above median) of the proinflammatory marker interleukin 6 were associated with higher PMD among all women (24.1% vs 18.5%, P = 0.007). Similarly, higher expression levels (above median) of the proinflammatory markers (interleukin 6, tumor necrosis factor-α, C-reactive protein, and interleukin 8) were associated with higher PMD among premenopausal women (absolute difference in the PMD of 8.8% [P = 0.006], 7.7% [P = 0.022], 6.7% [P = 0.037], and 16.5% [P = 0.032], respectively). Higher expression levels (above median) of the anti-inflammatory marker transforming growth factor-β were associated with lower PMD among all (18.8% vs 24.3%, P = 0.005) and postmenopausal women (14.5% vs 20.7%, P = 0.013). Our results provide support for the hypothesized role of inflammatory markers in breast carcinogenesis through their effects on mammographic density. Inflammatory markers could be targeted in future breast cancer prevention interventions.

Association of Catechol-O-methyltransferase polymorphism Val158Met and mammographic density: A meta-analysis

ObjectivesThe Val158Met polymorphism in catechol-O-methyltransferase (COMT) enzyme reduces the methylation of catechol estrogens, which may affect mammographic density. High mammographic density is a known risk factor of breast cancer. Our aim was to perform meta-analysis of the effect of COMT Val158Met polymorphism on mammographic density. MethodsOriginal studies reporting data on mammographic density, stratified by the presence of COMT Val158Met polymorphism, were identified and combined using genetic models Met/Val vs. Val/Val, Met/Met vs. Val/Val, Val/Met+Met/Met vs. Val/Val (dominant model) and Met/Met vs. Val/Met+Val/Val (recessive model). Subgroup analyses by breast cancer status, menopausal status and use of hormone replacement therapy (HRT) were also performed. ResultsEight studies were included in the meta-analysis. The overall effect in percent mammographic density was −1.41 (CI −2.86 to 0.05; P=0.06) in the recessive model. Exclusion of breast cancer patients increased the effect size to −1.93 (CI −3.49 to −0.37; P=0.02). The results suggested opposite effect of COMT Val158Met for postmenopausal users of HRT versus premenopausal women or postmenopausal non-users of HRT. ConclusionsCOMT Val158Met polymorphism may be associated with mammographic density at least in healthy women. Menopausal status and HRT should be taken into account in future studies to avoid masking of the underlying effects.