Search

Purpose: 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., Methods: 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., Results: 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., Conclusions: 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.

We evaluated whether a 76-locus polygenic risk score (PRS) and Breast Imaging Reporting and Data System (BI-RADS) breast density were independent risk factors within three studies (1643 case patients, 2397 control patients) using logistic regression models. We incorporated the PRS odds ratio (OR) into the Breast Cancer Surveillance Consortium (BCSC) risk-prediction model while accounting for its attributable risk and compared five-year absolute risk predictions between models using area under the curve (AUC) statistics. All statistical tests were two-sided. BI-RADS density and PRS were independent risk factors across all three studies (P interaction = .23). Relative to those with scattered fibroglandular densities and average PRS (2(nd) quartile), women with extreme density and highest quartile PRS had 2.7-fold (95% confidence interval [CI] = 1.74 to 4.12) increased risk, while those with low density and PRS had reduced risk (OR = 0.30, 95% CI = 0.18 to 0.51). PRS added independent information (P < .001) to the BCSC model and improved discriminatory accuracy from AUC = 0.66 to AUC = 0.69. Although the BCSC-PRS model was well calibrated in case-control data, independent cohort data are needed to test calibration in the general population., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Pregnancy and breastfeeding are major factors reducing breast cancer (BC) risk. A potential mechanism for this effect might be changes in mammographic density, but other factors might be involved. The aim of this study was to investigate factors influencing changes in breast size and breast stiffness after pregnancy. Of a consecutive cohort of 5991 women who gave birth between 1996 and 1999, 559 replied to a questionnaire including questions about breast changes. The women completed their own assessments of changes in breast size and stiffness since their last pregnancy. Factors being investigated regarding their predictive value for these changes were: BMI before pregnancy, weight gain, age at first full-term pregnancy (FFTP), number of pregnancies, breastfeeding, and BMI of the children's fathers. A decrease in breast size was reported in 21.8% of the participants and an increase in 35.1%. With regard to the breast stiffness, 66.4% reported a decrease and only 5% reported an increase. Independent predictors for increased breast size were age at FFTP, increase in BMI since last pregnancy, BMI before pregnancy, and time since FFTP. Factors predictive of greater breast stiffness included age at FFTP, BMI before FFTP, time since FFTP, breastfeeding status, and number of pregnancies. Breast changes after pregnancy depend on several variables, which are described as BC-risk factors. Individual reaction of the female breast to a pregnancy leads to different outcomes with regard to breast size and stiffness. Further studies are needed to clarify whether these individual responses interact with the effect of pregnancy on the BC risk.

Background: Mammographic density adjusted for age and body mass index (BMI) is a heritable marker of breast cancer susceptibility. Little is known about the biologic mechanisms underlying the association between mammographic density and breast cancer risk. We examined whether common low-penetrance breast cancer susceptibility variants contribute to interindividual differences in mammographic density measures., Methods: We established an international consortium (DENSNP) of 19 studies from 10 countries, comprising 16,895 Caucasian women, to conduct a pooled cross-sectional analysis of common breast cancer susceptibility variants in 14 independent loci and mammographic density measures. Dense and nondense areas, and percent density, were measured using interactive-thresholding techniques. Mixed linear models were used to assess the association between genetic variants and the square roots of mammographic density measures adjusted for study, age, case status, BMI, and menopausal status., Results: Consistent with their breast cancer associations, the C-allele of rs3817198 in LSP1 was positively associated with both adjusted dense area (P = 0.00005) and adjusted percent density (P = 0.001), whereas the A-allele of rs10483813 in RAD51L1 was inversely associated with adjusted percent density (P = 0.003), but not with adjusted dense area (P = 0.07)., Conclusion: We identified two common breast cancer susceptibility variants associated with mammographic measures of radiodense tissue in the breast gland., Impact: We examined the association of 14 established breast cancer susceptibility loci with mammographic density phenotypes within a large genetic consortium and identified two breast cancer susceptibility variants, LSP1-rs3817198 and RAD51L1-rs10483813, associated with mammographic measures and in the same direction as the breast cancer association., (©2012 AACR)

Background: Breast volume is a relevant measure for the prevention and prediction of diseases and for aesthetic surgery. This study evaluated a new technique to determine breast volume and compared measures using a three-dimensional (3D) body surface scanner and magnetic resonance imaging (MRI) scans, with the latter used as the standard method., Methods: Both MRI scans and body surface 3D scans were obtained from 22 women. For each method, breast volumes were assessed. The MRI calculations of the breast volumes were performed by a specially trained radiologist using analysis software. A textured 3D image was generated by a calibrated digital texture camera after breast surface data acquisition. The volume assessment of the 3D photography was calculated using a software package after manual outlining of the breast and automated projection of a dorsal limit. Linear regression was used to predict the MRI volume assessment with the 3D image volume assessment., Results: The mean breast volume according to MRI volumetry was 442.8 ml on the left side and 471.8 ml on the right side. The mean breast volume using a 3D body surface volume assessment method was 273.8 ml (observer A) and 226.2 ml (observer B) on the left side and 284.4 ml (observer A) and 234.9 ml (observer B) on the right side. The use of linear regression models showed R (2) values of 0.59-0.77. The mean time for MRI recording and volume assessment was 68.0 ± 14.1 min for both sides and 11.6 ± 1.5 min for 3D recording and volume assessment., Conclusions: The 3D surface-based volume measurements are feasible in terms of time and can predict the MRI breast volume with sufficient accuracy. This might facilitate the broad use of such an assessment technique in a large-scale epidemiologic study using breast volume as a study aim. Additionally, further development of volume assessments could help to implement this technique in breast surgery procedures.

Mammographic percent density (MD) is recognized as one of the strongest risk factors associated with breast cancer. This matched case-control study investigated whether MD represents an independent risk factor. Mammograms were obtained from 1025 breast cancer patients and from 520 healthy controls. MD was measured using a quantitative computer-based threshold method (0-100%). Breast cancer patients had a higher MD than healthy controls (38 vs. 32%, P<0.01). MD was significantly higher in association with factors such as age over 60 years, body mass index (BMI) of 25-30 kg/m², nulliparity or low parity (one to two births). Average MD was inversely associated with age, BMI, parity and positively associated with age at first full-term pregnancy. MD was higher in women with at least one first-degree relative affected, but only among patients and not in the group of healthy controls (P<0.01/P=0.61). In women with an MD of 25% or more, the risk of breast cancer was doubled compared with women with an MD of less than 10% (odds ratio: 2.1; 95% confidence interval: 1.3-3.4; P<0.01); in the postmenopausal subgroup, the risk was nearly tripled (odds ratio: 2.7; 95% confidence interval: 1.6-4.7; P<0.001). This study provides further evidence that MD is an important risk factor for breast cancer. These results indicate strong associations between MD and the risk of breast cancer in a matched case-control study in Germany.

Mammographic density (MD) has consistently been found as one of the strongest breast cancer risk factors. In our study, both qualitative and quantitative density measurements were performed in a hospital-based group of premenopausal women before and after first full-term pregnancy providing an opportunity for direct evaluation of the effects of one pregnancy on MD. Mammograms were obtained from 23 women before and after first full-term pregnancy and from 28 nulliparous controls. MD was determined by a standard qualitative assessment method using the Breast Imaging Reporting and Data System, and a quantitative computer-based threshold method (0-100%). The mean age at mammography before and after pregnancy was 31 and 34 years, respectively, with a mean difference of 40 months between mammographies. The quantitative density assessment showed a significant reduction in relative MD after pregnancy of 12 percentage points (8.6-15.4), compared with 3.1 (0.0-6.2) in the nulliparous control group (P<0.001). A reduction in MD of more than 10% was seen in 52% of the patients, compared with 18% of the controls. The qualitative density assessment confirmed a reduction in MD after pregnancy by one Breast Imaging Reporting and Data System category (P=0.02). This longitudinal study showed that MD can be influenced by one full-term pregnancy. This effect was seen with both quantitative and qualitative assessment methods. It may be hypothesized that breast cancer risk reduction associated with pregnancy is mediated through a direct reduction of MD, and MD assessment might be incorporated in individualizing risk assessment and prevention.