A prospective study of breast cancer risk using routine mammographic breast density measurements.

Mammographic breast density is a major risk factor for breast cancer but estimates of the relative risk associated with differing density patterns have varied widely. It is also unclear how menopausal status influences this association and to what extent the effects of density are due to its correlation with other risk factors. Most recent investigations of breast density have been case-control studies, which provide indirect estimates of relative risk. We have prospectively followed 61,844 women for an average of 3.1 years to directly estimate risk among women in the four mammographic breast density categories defined by the American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS). The study was population-based and used density assessments routinely made by community radiologists. Cox regression was used to obtain age-adjusted relative risk estimates and to control for other risk factors. Risk increased with density and the risk associated with extremely dense breasts, relative to entirely fatty breasts, was 4.6 (95% confidence interval, 1.7-12.6) for premenopausal women and 3.9 (95% confidence interval, 2.6-5.8) for postmenopausal women. Estimates for pre- and postmenopausal women did not differ significantly. Although breast density was significantly related to body mass index, age at first childbirth, and postmenopausal hormone use (P < 0.001), adjustment for these variables only slightly altered the relative risk estimates. Our results correspond well to those from case-control studies using more quantitative measures of mammographic breast density and suggest that routine Breast Imaging Reporting and Data System density measurements may be useful in models for assessing breast cancer risk in individual women.     

Columnar cell lesions,mammographic density and breast cancer risk

Background Mammographic density is the third largest risk factor for ductal carcinoma in-situ (DCIS) and invasive breast cancer. However, the question of whether risk-mediating precursor histological changes, such as columnar cell lesions (CCLs), can be found in dense but non-malignant breast tissues has not been systematically addressed. We hypothesized that CCLs may be related to breast composition, in particular breast density, in non-tumour containing breast tissue. Patients and methods We examined randomly selected tissue samples obtained by bilateral subcutaneous mastectomy from a forensic autopsy series, where tissue composition was assessed, and in which there had been no selection of subjects or histological specimens for breast disease. We reviewed H&E slides for the presence of atypical and non-atypical CCLs and correlated with histological features measured using quantitative microscopy. Results CCLs were seen in 40 out of 236 cases (17%). The presence of CCLs was found to be associated with several measures of breast tissue composition, including radiographic density: high Faxitron Wolfe Density (P = 0.037), high density estimated by percentage non-adipose tissue area (P = 0.037), high percentage collagen (P = 9.2E−05) and high percentage glandular area (P = 2E−05). DCIS was identified in two atypical CCL cases. The extent of CCL was not associated with any of the examined variables. Conclusion Our study is the first to report a possible association between CCLs and breast tissue composition, including mammographic density. Our data suggest that prospective elucidation of the strength and nature of the clinicopathological correlation may lead to an enhanced understanding of mammographic density and evidence based management strategies. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Longitudinal trends in mammographic percent density and breast cancer risk.

BACKGROUND: Mammographic density is a strong risk factor for breast cancer. However, whether changes in mammographic density are associated with risk remains unclear. MATERIALS AND METHODS: A study of 372 incident breast cancer cases and 713 matched controls was conducted within the Mayo Clinic mammography screening practice. Controls were matched on age, exam date, residence, menopause, interval between, and number of mammograms. All serial craniocaudal mammograms 10 years before ascertainment were digitized, and quantitative measures of percent density (PD) were estimated using a thresholding method. Data on potential confounders were abstracted from medical records. Logistic regression models with generalized estimating equations were used to evaluate the interactions among PD at earliest mammogram, time from earliest to each serial mammogram, and absolute change in PD between the earliest and subsequent mammograms. Analyses were done separately for PD measures from the ipsilateral and contralateral breast and also by use of hormone therapy (HT). RESULTS: Subjects had an average of five mammograms available, were primarily postmenopausal (83%), and averaged 61 years at the earliest mammogram. Mean PD at earliest mammogram was higher for cases (31%) than controls (27%; ipsilateral side). There was no evidence of an association between change in PD and breast cancer risk by time. Compared with no change, an overall reduction of 10% PD (lowest quartile of change) was associated with an odds ratio of 0.9997 and an increase of 6.5% PD (highest quartile of change) with an odds ratio of 1.002. The same results held within the group of 220 cases and 340 controls never using HT. Among the 124 cases and 337 controls known to use HT during the interval, there was a statistically significant interaction between change in PD and time since the earliest mammogram (P = 0.01). However, in all groups, the risk associated with the earliest PD remained a stronger predictor of risk than change in PD. CONCLUSION: We observed no association between change in PD with breast cancer risk among all women and those never using HT. However, the interaction between change in PD and time should be evaluated in other populations.     

Body size, mammographic density, and breast cancer risk.

BACKGROUND: Greater weight and body mass index (BMI) are negatively correlated with mammographic density, a strong risk factor for breast cancer, and are associated with an increased risk of breast cancer in postmenopausal women, but with a reduced risk in premenopausal women. We have examined the associations of body size and mammographic density on breast cancer risk. METHOD: We examined the associations of body size and the percentage of mammographic density at baseline with subsequent risk of breast cancer among 1,114 matched case-control pairs identified from three screening programs. The effect of each factor on risk of breast cancer was examined before and after adjustment for the other, using logistic regression. RESULTS: In all subjects, before adjustment for mammographic density, breast cancer risk in the highest quintile of BMI, compared with the lowest, was 1.04 [95% confidence interval (CI), 0.8-1.4]. BMI was associated positively with breast cancer risk in postmenopausal women, and negatively in premenopausal women. After adjustment for density, the risk associated with BMI in all subjects increased to 1.60 (95% CI, 1.2-2.2), and was positive in both menopausal groups. Adjustment for BMI increased breast cancer risk in women with 75% or greater density, compared with 0%, increased from 4.25 (95% CI, 1.6-11.1) to 5.86 (95% CI, 2.2-15.6). CONCLUSION: BMI and mammographic density are independent risk factors for breast cancer, and likely to operate through different pathways. The strong negative correlated between them will lead to underestimation of the effects on risk of either pathway if confounding is not controlled.     

Changes in mammographic breast density and concomitant changes in breast cancer risk.

Among participants of the biennial Nijmegen breast cancer screening programme, we examined whether diminution of mammographic breast density lowered breast cancer risk. Post-menopausal breast cancer cases (n = 108), who had to have participated in all the five screening rounds prior to their diagnosis, were matched to 400 controls on year of birth and screening history. Controls had to be free of breast cancer at the time of the case's diagnosis. Changes in breast density were measured over a 10-year period, by a fully computerized method. Women in whom 5-25% or >25% of the breast was composed of fibro-glandular density showed a threefold increased 10-year risk compared to women with <5% density. In women with 5-25% density initially, we observed a trend of decreasing risk with diminishing density: when women with <5% density throughout the whole period formed the reference category, the odds ratio (OR) for those who decreased from 5-25% to <5% density was 1.9 [95% confidence interval (CI) = 0.6-6.1] in contrast to the OR of 5.7 (95% CI = 2.2-15.2) for those with persisting 5-25% density. In women who increased from 5-25% density to >25% density the OR was 6.9 (95% CI = 2.1-22.9). In women with >25% density initially, diminishing density was not clearly associated with lowering risk, which may be partly explained by the low number of women who decreased to <5% (n = 12). Due to the limited size of the study these results have to be interpreted with caution. Although the results are not conclusive, they could indicate a trend of decreasing risk with diminishing breast density. Should this effect be real, it may have great implications for the primary prevention of breast cancer or for the identification of high-risk groups who would benefit by more frequent screening. Therefore, large-scale, long-term follow-up studies on the effects of changes in breast density are needed.     

Does breast size modify the association between mammographic density and breast cancer risk?

BACKGROUND: Both the absolute and the percent of mammographic density are strong and independent risk factors for breast cancer. Previously, we showed that the association between mammographic density and breast cancer risk tended to be weaker in African American than in White U.S. women. Because African American women have a larger breast size, we assessed whether the association between mammographic density and breast cancer was less apparent in large than in small breasts. METHODS: We assessed mammographic density on mammograms from 348 African American and 507 White women, 479 breast cancer patients and 376 control subjects, from a case-control study conducted in Los Angeles County. We estimated odds ratios (OR) for breast cancer with increasing mammographic density, and the analyses were stratified by mammographic breast area. RESULTS: Median breast size was 168.4 cm2 in African American women and 121.7 cm2 in White women (P for difference <0.001). For absolute density, adjusted ORs (95% confidence intervals) per increase of 10 cm2 were 1.32 (1.13-1.54), 1.14 (1.03-1.26), and 1.02 (0.98-1.07) in the first, second, and third tertiles of breast area, respectively (P for effect modification by breast area = 0.005). The results for percent density were similar although weaker; adjusted ORs per 10% increase (absolute value) in percent density were 1.22 (1.05-1.40), 1.22 (1.06-1.41), and 1.03 (0.90-1.18 P for effect modification by breast area = 0.34). CONCLUSION: Our results indicate that the association between mammographic density and breast cancer may be weaker in women with larger breasts.     

Postmenopausal mammographic breast density and subsequent breast cancer risk according to selected tissue markers

Background:

This study aimed to determine if associations of pre-diagnostic percent breast density, absolute dense area, and non-dense area with subsequent breast cancer risk differ by the tumour''s molecular marker status.

Methods:

We included 1010 postmenopausal women with breast cancer and 2077 matched controls from the Nurses'' Health Study (NHS) and the Nurses'' Health Study II (NHS II) cohorts. Breast density was estimated from digitised film mammograms using computer-assisted thresholding techniques. Information on breast cancer risk factors was obtained prospectively from biennial questionnaires. Polychotomous logistic regression was used to assess associations of breast density measures with tumour subtypes by the status of selected tissue markers. All tests of statistical significance were two sided.

Results:

The association of percent density with breast cancer risk appeared to be stronger in ER− as compared with ER+ tumours, but the difference did not reach statistical significance (density ⩾50% vs <10% odds ratio (OR)=3.06, 95% confidence interval (CI) 2.17–4.32 for ER+ OR=4.61, 95% CI 2.36–9.03 for ER−, P_{heterogeneity}=0.08). Stronger positive associations were found for absolute dense area and CK5/6− and EGFR− as compared with respective marker-positive tumours (P_{heterogeneity}=0.002 and 0.001, respectively). Stronger inverse associations of non-dense area with breast cancer risk were found for ER− as compared with ER+ tumours (P_{heterogeneity}=0.0001) and for AR+, CK5/6+, and EGFR+ as compared with respective marker-negative tumours (P_{heterogeneity}=0.03, 0.005, and 0.009, respectively). The associations of density measures with breast cancer did not differ by progesterone receptor and human epidermal growth factor receptor 2 status.

Conclusions:

Breast density influences the risk of breast cancer subtypes by potentially different mechanisms.     

Relationship between breast cancer risk factors and mammographic breast density in the Fernald Community Cohort

Background:

We investigated associations of known breast cancer risk factors with breast density, a well-established and very strong predictor of breast cancer risk.

Methods:

This nested case–control study included breast cancer-free women, 265 with high and 860 with low breast density. Women were required to be 40–80 years old and should have a body mass index (BMI) <35 at the time of the index mammogram. Information on covariates was obtained from annual questionnaires.

Results:

In the overall analysis, breast density was inversely associated with BMI at mammogram (P for trend<0.001), and parity (P for trend=0.02) and positively associated with alcohol consumption (ever vs never: odds ratio 2.0, 95% confidence interval 1.4–2.8). Alcohol consumption was positively associated with density, and the association was stronger in women with a family history of breast cancer (P<0.001) and in women with hormone replacement therapy (HRT) history (P<0.001). Parity was inversely associated with density in all subsets, except premenopausal women and women without a family history. The association of parity with density was stronger in women with HRT history (P<0.001).

Conclusion:

The associations of alcohol and parity with breast density appear to be in reverse direction, but stronger in women with a family history of breast cancer and women who ever used HRT.     

Adjusting for BMI in analyses of volumetric mammographic density and breast cancer risk

Background

Fully automated assessment of mammographic density (MD), a biomarker of breast cancer risk, is being increasingly performed in screening settings. However, data on body mass index (BMI), a confounder of the MD–risk association, are not routinely collected at screening. We investigated whether the amount of fat in the breast, as captured by the amount of mammographic non-dense tissue seen on the mammographic image, can be used as a proxy for BMI when data on the latter are unavailable.

Methods

Data from a UK case control study (numbers of cases/controls: 414/685) and a Norwegian cohort study (numbers of cases/non-cases: 657/61059), both with volumetric MD measurements (dense volume (DV), non-dense volume (NDV) and percent density (%MD)) from screening-age women, were analysed. BMI (self-reported) and NDV were taken as measures of adiposity. Correlations between BMI and NDV, %MD and DV were examined after log-transformation and adjustment for age, menopausal status and parity.Logistic regression models were fitted to the UK study, and Cox regression models to the Norwegian study, to assess associations between MD and breast cancer risk, expressed as odds/hazard ratios per adjusted standard deviation (OPERA). Adjustments were first made for standard risk factors except BMI (minimally adjusted models) and then also for BMI or NDV. OPERA pooled relative risks (RRs) were estimated by fixed-effect models, and between-study heterogeneity was assessed by the I² statistics.

Results

BMI was positively correlated with NDV (adjusted r = 0.74 in the UK study and r = 0.72 in the Norwegian study) and with DV (r = 0.33 and r = 0.25, respectively). Both %MD and DV were positively associated with breast cancer risk in minimally adjusted models (pooled OPERA RR (95% confidence interval): 1.34 (1.25, 1.43) and 1.46 (1.36, 1.56), respectively; I² = 0%, P >0.48 for both). Further adjustment for BMI or NDV strengthened the %MD–risk association (1.51 (1.41, 1.61); I² = 0%, P = 0.33 and 1.51 (1.41, 1.61); I² = 0%, P = 0.32, respectively). Adjusting for BMI or NDV marginally affected the magnitude of the DV–risk association (1.44 (1.34, 1.54); I² = 0%, P = 0.87 and 1.49 (1.40, 1.60); I² = 0%, P = 0.36, respectively).

Conclusions

When volumetric MD–breast cancer risk associations are investigated, NDV can be used as a measure of adiposity when BMI data are unavailable.

     

Breast cancer risk factors and mammographic breast density in women over age 70

SummaryBackground Breast density is a strong risk factor for breast cancer, but little is known about factors associated with breast density in women over 70.Methods Percent breast density, sex hormone levels and breast cancer risk factor data were obtained on 239 women ages 70–92 recruited from 1986 to 1988 in the United States. Multivariable linear regression was used to develop a model to describe factors associated with percent density.Results Median (range) percent density among women was 23.7% (0–85%). Body mass index (β= −0.345, p<0.001 adjusted for age and parity) and parity (β= −0.277, p<0.001 adjusted for age and BMI) were significantly and inversely associated with percent breast density. After adjusting for parity and BMI, age was not associated with breast density (β=0.05, p=0.45). Parous women had lower percent density than nulliparous women (23.7 versus 34.7%, p=0.005). Women who had undergone surgical menopause had greater breast density than those who had had a natural menopause (33.4 versus 24.8%, p=0.048), as did women who were not current smokers (26.0 versus 17.3% for smokers, p=0.02). Breast density was not associated with age at menarche, age at menopause, age at first birth, breastfeeding, estrogen levels or androgen levels. In a multivariable model, 24% of the variance in percent breast density was explained by BMI (β= −0.35), parity (β=−0.29), surgical menopause (β=0.13) and current smoking (β= −0.12).Conclusion Factors associated with breast density in older, post-menopausal women differ from traditional breast cancer risk factors and from factors associated with breast density in pre-menopausal and younger post-menopausal women.     

Predicting interval and screen-detected breast cancers from mammographic density defined by different brightness thresholds

Background

Case–control studies show that mammographic density is a better risk factor when defined at higher than conventional pixel-brightness thresholds. We asked if this applied to interval and/or screen-detected cancers.

Method

We conducted a nested case–control study within the prospective Melbourne Collaborative Cohort Study including 168 women with interval and 422 with screen-detected breast cancers, and 498 and 1197 matched controls, respectively. We measured absolute and percent mammographic density using the Cumulus software at the conventional threshold (Cumulus) and two increasingly higher thresholds (Altocumulus and Cirrocumulus, respectively). Measures were transformed and adjusted for age and body mass index (BMI). Using conditional logistic regression and adjusting for BMI by age at mammogram, we estimated risk discrimination by the odds ratio per adjusted standard deviation (OPERA), calculated the area under the receiver operating characteristic curve (AUC) and compared nested models using the likelihood ratio criterion and models with the same number of parameters using the difference in Bayesian information criterion (ΔBIC).

Results

For interval cancer, there was very strong evidence that the association was best predicted by Cumulus as a percentage (OPERA?=?2.33 (95% confidence interval (CI) 1.85–2.92); all ΔBIC >?14), and the association with BMI was independent of age at mammogram. After adjusting for percent Cumulus, no other measure was associated with risk (all P?>?0.1). For screen-detected cancer, however, the associations were strongest for the absolute and percent Cirrocumulus measures (all ΔBIC >?6), and after adjusting for Cirrocumulus, no other measure was associated with risk (all P?>?0.07).

Conclusion

The amount of brighter areas is the best mammogram-based measure of screen-detected breast cancer risk, while the percentage of the breast covered by white or bright areas is the best mammogram-based measure of interval breast cancer risk, irrespective of BMI. Therefore, there are different features of mammographic images that give clinically important information about different outcomes.

     

Background risk of breast cancer and the association between physical activity and mammographic density

Introduction

High physical activity has been shown to decrease the risk of breast cancer, potentially by a mechanism that also reduces mammographic density. We tested the hypothesis that the risk of developing breast cancer in the next 10 years according to the Tyrer-Cuzick prediction model influences the association between physical activity and mammographic density.

Methods

We conducted a population-based cross-sectional study of 38,913 Swedish women aged 40–74 years. Physical activity was assessed using the validated web-questionnaire Active-Q and mammographic density was measured by the fully automated volumetric Volpara method. The 10-year risk of breast cancer was estimated using the Tyrer-Cuzick (TC) prediction model. Linear regression analyses were performed to assess the association between physical activity and volumetric mammographic density and the potential interaction with the TC breast cancer risk.

Results

Overall, high physical activity was associated with lower absolute dense volume. As compared to women with the lowest total activity level (<40 metabolic equivalent hours [MET-h] per day), women with the highest total activity level (≥50 MET-h/day) had an estimated 3.4 cm³ (95% confidence interval, 2.3-4.7) lower absolute dense volume. The inverse association was seen for any type of physical activity among women with <3.0% TC 10-year risk, but only for total and vigorous activities among women with 3.0-4.9% TC risk, and only for vigorous activity among women with ≥5.0% TC risk. The association between total activity and absolute dense volume was modified by the TC breast cancer risk (P_interaction = 0.05). As anticipated, high physical activity was also associated with lower non-dense volume. No consistent association was found between physical activity and percent dense volume.

Conclusions

Our results suggest that physical activity may decrease breast cancer risk through reducing mammographic density, and that the physical activity needed to reduce mammographic density may depend on background risk of breast cancer.     

The influence of mammogram acquisition on the mammographic density and breast cancer association in the mayo mammography health study cohort

Introduction

Mammographic density is a strong risk factor for breast cancer. Image acquisition technique varies across mammograms to limit radiation and produce a clinically useful image. We examined whether acquisition technique parameters at the time of mammography were associated with mammographic density and whether the acquisition parameters confounded the density and breast cancer association.

Methods

We examined this question within the Mayo Mammography Health Study (MMHS) cohort, comprised of 19,924 women (51.2% of eligible) seen in the Mayo Clinic mammography screening practice from 2003 to 2006. A case-cohort design, comprising 318 incident breast cancers diagnosed through December 2009 and a random subcohort of 2,259, was used to examine potential confounding of mammogram acquisition technique parameters (x-ray tube voltage peak (kVp), milliampere-seconds (mAs), thickness and compression force) on the density and breast cancer association. The Breast Imaging Reporting and Data System four-category tissue composition measure (BI-RADS) and percent density (PD) (Cumulus program) were estimated from screen-film mammograms at time of enrollment. Spearman correlation coefficients (r) and means (standard deviations) were used to examine the relationship of density measures with acquisition parameters. Hazard ratios (HR) and C-statistics were estimated using Cox proportional hazards regression, adjusting for age, menopausal status, body mass index and postmenopausal hormones. A change in the HR of at least 15% indicated confounding.

Results

Adjusted PD and BI-RADS density were associated with breast cancer (p-trends < 0.001), with a 3 to 4-fold increased risk in the extremely dense vs. fatty BI-RADS categories (HR: 3.0, 95% CI, 1.7 - 5.1) and the ≥ 25% vs. ≤ 5% PD categories (HR: 3.8, 95% CI, 2.5 - 5.9). Of the acquisition parameters, kVp was not correlated with PD (r = 0.04, p = 0.07). Although thickness (r = -0.27, p < 0.001), compression force (r = -0.16, p < 0.001), and mAs (r = -0.06, p = 0.008) were inversely correlated with PD, they did not confound the PD or BI-RADS associations with breast cancer and their inclusion did not improve discriminatory accuracy. Results were similar for associations of dense and non-dense area with breast cancer.

Conclusions

We confirmed a strong association between mammographic density and breast cancer risk that was not confounded by mammogram acquisition technique.     

Parity and mammographic breast density in relation to breast cancer risk: indication of interaction.

We examined whether the harmful influence of nulliparity on breast cancer risk could be mediated by high mammographic density. Another possibility is that mammographic density and nulliparity act independently or perhaps synergistically on breast cancer risk. Our study population consisted of 129 cases and 517 controls who had been participants in the Nijmegen breast cancer screening programme for 10 years. Breast density was classified with a fully automated technique on digitized mammograms from the screening examination 10 years before diagnosis. Classification was based on the proportion of the breast that was composed of high density: < 5%, 5-25% or > 25%. Data on parity and potential confounders were obtained using a questionnaire, administered at the same examination. We found that nulliparae with low breast density (< 5%) were not at increased risk compared to parous women with low density: OR 1.1 (95% CI 0.2-5.8). Parous women with < 5% density formed the reference category throughout all analyses. The risks for parous women with 5-25% or > 25% density were 2.7 (95% CI 1.3-5.6) and 3.6 (95% CI 1.7-7.7) fold increased, respectively. However, when both factors were present (nulliparity and > or = 5% density), breast cancer risk was 7.1 times higher (95% CI 3.2-15.9). This could indicate that nulliparity and high breast density might work synergistically and that breast density is not just an explanatory factor in the influence of nulliparity on breast cancer risk. It is hypothesized that high breast density (reflecting fibro-glandular tissue with increased epithelial cell proliferation) is more susceptible to carcinogenic effects in the undifferentiated epithelial breast tissue of nulliparae than in the differentiated tissue of parous women. Since there were few data, no firm conclusions can be drawn. If these findings can be confirmed in a larger study population, however, they may have important implications for the prevention and early detection of breast cancer.     

Changes in mammographic density over time in breast cancer cases and women at high risk for breast cancer

High mammographic breast density is one of the strongest intermediate markers of breast cancer risk, and decreases in density over time have been associated with decreases in breast cancer risk. Using repeated measures of mammographic density in a cohort of high‐risk women, the Women at Risk (WAR) cohort at Columbia University Medical Center (N = 2670), we examined whether changes in prediagnostic mammographic density differed among 85 prospectively‐ascertained breast cancer cases and 85 age‐matched controls, using a nested case–control design. Median age at first mammogram was 51 years (range, 29–77 years), with a median of 4 years between first and second prediagnostic mammogram (range, 1–15 years). Using linear regression with change in percent density as the outcome, we found that in women who did not go on to be diagnosed with breast cancer, change in percent density decreased as time between first and second mammogram increased (β = ?1.62% per year, p = 0.004). However, in women who did go on to be diagnosed with breast cancer, there was no overall change in percent density associated with time between first and second mammogram (β = 0.29% per year, p = 0.61); the change over time was statistically significantly different between cases versus controls (p <0.009). If replicated in larger cohorts, these results suggest that within‐individual changes in mammographic density as measured by percent density may be a useful biomarker of breast cancer risk.     

Effects of mammographic density and benign breast disease on breast cancer risk (United States)

Background: Having either a history of benign breast disease, particularly atypical hyperplasia or extensive mammographic breast density, is associated with increased breast cancer risk. Previous studies have described an association between benign breast disease histology and breast density. However, whether these features measure the same risk, or are independent risk factors, has not been addressed. Methods: This case–control study, nested within the prospective follow-up of the Breast Cancer Detection Demonstration Project, evaluated both benign histologic and mammographic density information from 347 women who later developed breast cancer and 410 age- and race-matched controls without breast cancer. Multivariate logistic regression analyses provided maximum-likelihood estimates of the odds ratios (OR) and 95% confidence intervals (CI) to evaluate the relative risk of breast cancer associated with each exposure. Results: Adjusting for mammographic density, the OR for atypical hyperplasia was 2.1 (95% CI: 1.3–3.6), and adjusting for benign breast histology, the OR for 75% density was 3.8 (95% CI: 2.0–7.2). Women with nonproliferative benign breast disease and 75% density had an OR of 5.8 (95% CI: 1.8–18.6), and women with <50% density and atypical hyperplasia had an OR of 4.1 (95% CI: 2.1–8.0). Conclusions: In this study, both benign breast disease histology and the percentage of the breast area with mammographic density were associated with breast cancer risk. However, women with both proliferative benign breast disease and 75% density were not at as high a risk of breast cancer due to the combination of effects (p = 0.002) as women with only one of these factors.     

Premenopausal plasma 25-hydroxyvitamin D,mammographic density,and risk of breast cancer

Breast Cancer Research and Treatment - Epidemiologic evidence for an association between plasma 25-hydroxyvitamin D [25(OH)D] and breast cancer is inconsistent. Data are especially limited for...