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

Current regular aspirin use and mammographic breast density: a cross-sectional analysis considering concurrent statin and metformin use

PURPOSE

The nonsteroidal anti-inflammatory drug aspirin is an agent of interest for breast cancer prevention. However, it is unclear if aspirin affects mammographic breast density (MBD), a marker of elevated breast cancer risk, particularly in the context of concurrent use of medications indicated for common cardiometabolic conditions, which may also be associated with MBD.

METHODS

We used data from the New York Mammographic Density Study for 770 women age 40-60 years old with no history of breast cancer. We evaluated the association between current regular aspirin use and MBD, using linear regression for continuous measures of absolute and percent dense areas and absolute non-dense area, adjusted for body mass index (BMI), sociodemographic and reproductive factors, and use of statins and metformin. We assessed effect modification by BMI and reproductive factors.

RESULTS

After adjustment for co-medication, current regular aspirin use was only positively associated with non-dense area (β = 18.1, 95% CI: 6.7, 29.5). Effect modification by BMI and parity showed current aspirin use to only be associated with larger non-dense area among women with a BMI ≥ 30 (β = 28.2, 95% CI: 10.8, 45.7), and with lower percent density among parous women (β = -3.3, 95% CI: -6.4, -0.3).

CONCLUSIONS

Independent of co-medication use, current regular aspirin users had greater non-dense area with stronger estimates for women with higher BMI. We found limited support for an association between current aspirin use and mammographically dense breast tissue among parous women.

Association of Interactions Between Mammographic Density Phenotypes and Established Risk Factors With Breast Cancer Risk, by Tumor Subtype and Menopausal Status

Previous studies suggest that the association between mammographic density (MD) and breast cancer risk might be modified by other breast cancer risk factors. In this study, we assessed multiplicative interactions between MD measures and established risk factors on the risk of invasive breast cancer overall and according to menopausal and estrogen receptor status. We used data on 2,137 cases and 4,346 controls from a nested case-control study within the Nurses' Health Study (1976-2004) and Nurses' Health Study II (1989-2007), whose data on percent mammographic density (PMD) and absolute area of dense tissue and nondense tissue (NDA) were available. No interaction remained statistically significant after adjusting for number of comparisons. For breast cancer overall, we observed nominally significant interactions (P < 0.05) between nulliparity and PMD/NDA, age at menarche and area of dense tissue, and body mass index and NDA. Individual nominally significant interactions across MD measures and risk factors were also observed in analyses stratified by either menopausal or estrogen receptor status. Our findings help provide further insights into potential mechanisms underlying the association between MD and breast cancer.

Parenchymal pattern in women with dense breasts. Variation with age and impact on screening outcomes: observations from a UK screening programme

OBJECTIVES

To assess patterns of parenchymal tissue on mammography in women with dense breasts and to determine how this varies with age and affects recall to assessment and cancer diagnosis.

METHOD

Breast density data was obtained in women attending routine mammographic screening from April 2013 to March 2015 using automated breast density assessment software. Women with the densest breasts were selected for visual interpretation of parenchymal pattern (PP). One hundred non-assessed women, aged 50, 55, 60, 65 and 69-71 years (total = 500), provided controls. Cases included women recalled for assessment (mastectomy or implants excluded) (total = 280). Mammograms reviewed by ten readers and PP classified as: (1) very smooth; (2) mainly smooth; (3) mixed; (4) mainly nodular; (5) very nodular. The ratio of women in each category at each age and screening outcomes were compared by Pearson's chi-squared test.

RESULTS

Reader agreement for scoring PP was good (intraclass correlation = 0.6302). Proportions of women in each PP category were similar at all ages for controls (p = 0.147) and cases (p = 0.657). The ratio of PP categories did not vary significantly with age in those who underwent biopsy (p = 0.484). Thirty-four cancers were diagnosed. There was a significant correlation between a diagnosis of cancer and nodular PP compared to not nodular PP (p = 0.043).

CONCLUSIONS

The ratio of smooth to nodular pattern in women with the densest breasts did not vary with age. The PP of the breast tissue did not affect likelihood of recall to assessment or biopsy. There was a significant relationship between a nodular parenchymal pattern and diagnosis of cancer.

KEY POINTS

• This paper shows that there is good agreement between mammogram readers when classifying mammographic PP on a five-point scale from very smooth to very nodular. • In non-assessed women with the densest breasts, there is no significant change in the proportions of smooth to nodular patterns with increasing age. • The likelihood of recall for further assessment or biopsy at assessment is not related to PP in women with highest breast density. • When recalled for further assessment, significantly more women are diagnosed with cancer in the group with nodular PP on mammography when compared with smooth and mixed patterns.

The Short-Term Effect of Weight Loss Surgery on Volumetric Breast Density and Fibroglandular Volume

Purpose

Obesity and breast density are both associated with an increased risk of breast cancer and are potentially modifiable. Weight loss surgery (WLS) causes a significant reduction in the amount of body fat and a decrease in breast cancer risk. The effect of WLS on breast density and its components has not been documented. Here, we analyze the impact of WLS on volumetric breast density (VBD) and on each of its components (fibroglandular volume and breast volume) by using three-dimensional methods.

Materials and Methods

Fibroglandular volume, breast volume, and their ratio, the VBD, were calculated from mammograms before and after WLS by using Volpara™ automated software.

Results

For the 80 women included, average body mass index decreased from 46.0 ± 7.22 to 33.7 ± 7.06 kg/m². Mammograms were performed on average 11.6 ± 9.4 months before and 10.1 ± 7 months after WLS. There was a significant reduction in average breast volume (39.4 % decrease) and average fibroglandular volume (15.5 % decrease), and thus, the average VBD increased from 5.15 to 7.87 % (p < 1 × 10⁻⁹) after WLS. When stratified by menopausal status and diabetic status, VBD increased significantly in all groups but only perimenopausal and postmenopausal women and non-diabetics experienced a significant reduction in fibroglandular volume.

Conclusions

Breast volume and fibroglandular volume decreased, and VBD increased following WLS, with the most significant change observed in postmenopausal women and non-diabetics. Further studies are warranted to determine how physical and biological alterations in breast density components after WLS may impact breast cancer risk.

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

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.

Polymorphisms in the estrogen receptor alpha gene (ESR1), daily cycling estrogen and mammographic density phenotypes

Background

Single nucleotide polymorphisms (SNPs) involved in the estrogen pathway and SNPs in the estrogen receptor alpha gene (ESR1 6q25) have been linked to breast cancer development, and mammographic density is an established breast cancer risk factor. Whether there is an association between daily estradiol levels, SNPs in ESR1 and premenopausal mammographic density phenotypes is unknown.

Methods

We assessed estradiol in daily saliva samples throughout an entire menstrual cycle in 202 healthy premenopausal women in the Norwegian Energy Balance and Breast Cancer Aspects I study. DNA was genotyped using the Illumina Golden Gate platform. Mammograms were taken between days 7 and 12 of the menstrual cycle, and digitized mammographic density was assessed using a computer-assisted method (Madena). Multivariable regression models were used to study the association between SNPs in ESR1, premenopausal mammographic density phenotypes and daily cycling estradiol.

Results

We observed inverse linear associations between the minor alleles of eight measured SNPs (rs3020364, rs2474148, rs12154178, rs2347867, rs6927072, rs2982712, rs3020407, rs9322335) and percent mammographic density (p-values: 0.002–0.026), these associations were strongest in lean women (BMI, ≤23.6 kg/m^2.). The odds of above-median percent mammographic density (>28.5 %) among women with major homozygous genotypes were 3–6 times higher than those of women with minor homozygous genotypes in seven SNPs. Women with rs3020364 major homozygous genotype had an OR of 6.46 for above-median percent mammographic density (OR: 6.46; 95 % Confidence Interval 1.61, 25.94) when compared to women with the minor homozygous genotype. These associations were not observed in relation to absolute mammographic density. No associations between SNPs and daily cycling estradiol were observed. However, we suggest, based on results of borderline significance (p values: 0.025–0.079) that the level of 17β-estradiol for women with the minor genotype for rs3020364, rs24744148 and rs2982712 were lower throughout the cycle in women with low (<28.5 %) percent mammographic density and higher in women with high (>28.5 %) percent mammographic density, when compared to women with the major genotype.

Conclusion

Our results support an association between eight selected SNPs in the ESR1 gene and percent mammographic density. The results need to be confirmed in larger studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-016-2804-1) contains supplementary material, which is available to authorized users.

Cyclic endogenous estrogen and progesterone vary by mammographic density phenotypes in premenopausal women

Estrogen and progesterone are key factors in the development of breast cancer, but it remains unclear whether these hormones are associated with mammographic density phenotypes in premenopausal women. We measured percent mammographic density, nondense area, and absolute mammographic density using computer-assisted breast density readings (Madena) from digitized mammograms taken on a scheduled day of the menstrual cycle (day 7-12) among 202 healthy, premenopausal women (Energy Balance and Breast cancer Aspects Study-I). Daily salivary concentrations of 17β-estradiol and progesterone throughout an entire menstrual cycle and fasting morning serum concentrations of hormones on 3 specific days of the menstrual cycle were assessed. Salivary and serum 17β-estradiol and progesterone were positively associated with percent mammographic density, we observed by 1 SD increase in overall salivary estradiol (β-value equal to 2.07, P=0.044), luteal salivary progesterone (β-value equal to 2.40, P=0.020). Women with above-median percent mammographic density had a 20% higher mean salivary 17β-estradiol level throughout the menstrual cycle. The odds ratio for having above-median percent mammographic density (>28.5%) per 1 SD increase in overall salivary 17β-estradiol was 1.66 (95% confidence interval 1.13-2.45). Women in the top tertile of the overall average daily 17β-estradiol concentrations had an odds ratio of 2.54 (confidence interval 1.05-6.16) of above-median percent mammographic density compared with women in the bottom tertile. Our finding of a relationship between estrogen, progesterone, and percent mammographic density and not with other mammographic density phenotypes in premenopausal women is biologically plausible, but needs to be replicated in larger studies.

Alcohol consumption, endogenous estrogen and mammographic density among premenopausal women

Introduction

Alcohol consumption may promote aromatization of androgens to estrogens, which may partly explain the observations linking alcohol consumption to higher breast cancer risk. Whether alcohol consumption is associated with endogenous estrogen levels, and mammographic density phenotypes in premenopausal women remains unclear.

Methods

Alcohol consumption was collected by self-report and interview, using semi quantitative food frequency questionnaires, and a food diary during seven days of a menstrual cycle among 202 premenopausal women, participating in the Energy Balance and Breast Cancer Aspects (EBBA) study I. Estrogen was assessed in serum and daily in saliva across an entire menstrual cycle. Computer-assisted mammographic density (Madena) was obtained from digitized mammograms taken between days 7–12 of the menstrual cycle. Multivariable regression models were used to investigate the associations between alcohol consumption, endogenous estrogen and mammographic density phenotypes.

Results

Current alcohol consumption was positively associated with endogenous estrogen, and absolute mammographic density. We observed 18 % higher mean salivary 17β-estradiol levels throughout the menstrual cycle, among women who consumed more than 10 g of alcohol per day compared to women who consumed less than 10 g of alcohol per day (p = 0.034). Long-term and past-year alcohol consumption was positively associated with mammographic density. We observed a positive association between alcohol consumption (past year) and absolute mammographic density; high alcohol consumers (≥7 drinks/week) had a mean absolute mammographic density of 46.17 cm² (95 % confidence interval (CI) 39.39, 52.95), while low alcohol consumers (<1 drink/week) had a mean absolute mammographic density of 31.26 cm² (95 % CI 25.89, 36.64) (p-trend 0.001). After adjustments, high consumers of alcohol (≥7 drinks/week), had 5.08 (95 % CI 1.82, 14.20) times higher odds of having absolute mammographic density above median (>32.4 cm²), compared to low (<1 drink/week) alcohol consumers.

Conclusion

Alcohol consumption was positively associated with daily endogenous estrogen levels and mammographic density in premenopausal women. These associations could point to an important area of breast cancer prevention.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-015-0620-1) contains supplementary material, which is available to authorized users.

High-density lipoprotein-cholesterol, daily estradiol and progesterone, and mammographic density phenotypes in premenopausal women

High-density lipoprotein-cholesterol (HDL-C) may influence the proliferation of breast tumor cells, but it is unclear whether low HDL-C levels, alone or in combination with cyclic estrogen and progesterone, are associated with mammographic density, a strong predictor of breast cancer development. Fasting morning serum concentrations of HDL-C were assessed in 202 premenopausal women, 25 to 35 years of age, participating in the Norwegian Energy Balance and Breast Cancer Aspects (EBBA) I study. Estrogen and progesterone were measured both in serum, and daily in saliva, throughout an entire menstrual cycle. Absolute and percent mammographic density was assessed by a computer-assisted method (Madena), from digitized mammograms (days 7-12). Multivariable models were used to study the associations between HDL-C, estrogen and progesterone, and mammographic density phenotypes. We observed a positive association between HDL-C and percent mammographic density after adjustments (P = 0.030). When combining HDL-C, estradiol, and progesterone, we observed among women with low HDL-C (<1.39 mmol/L), a linear association between salivary 17β-estradiol, progesterone, and percent and absolute mammographic density. Furthermore, in women with low HDL-C, each one SD increase of salivary mid-menstrual 17β-estradiol was associated with an OR of 4.12 (95% confidence intervals; CI, 1.30-13.0) of having above-median percent (28.5%), and an OR of 2.5 (95% CI, 1.13-5.50) of having above-median absolute mammographic density (32.4 cm(2)). On the basis of plausible biologic mechanisms linking HDL-C to breast cancer development, our findings suggest a role of HDL-C, alone or in combination with estrogen, in breast cancer development. However, our small hypothesis generating study requires confirmation in larger studies.

Mammographic breast density and breast cancer risk: interactions of percent density, absolute dense, and non-dense areas with breast cancer risk factors

We investigated if associations of breast density and breast cancer differ according to the level of other known breast cancer risk factors, including body mass index (BMI), age at menarche, parity, age at first child's birth, age at menopause, alcohol consumption, a family history of breast cancer, a history of benign breast disease, and physical activity. This study included 1,044 postmenopausal incident breast cancer cases diagnosed within the Nurses' Health Study cohort and 1,794 matched controls. Percent breast density, absolute dense, and non-dense areas were measured from digitized film images with computerized techniques. Information on breast cancer risk factors was obtained prospectively from biennial questionnaires. Percent breast density was more strongly associated with breast cancer risk in current postmenopausal hormone users (≥50 vs. 10 %: OR 5.34, 95 % CI 3.36-8.49) as compared to women with past (OR 2.69, 95 % CI 1.32-5.49) or no hormone history (OR 2.57, 95 % CI 1.18-5.60, p-interaction = 0.03). Non-dense area was inversely associated with breast cancer risk in parous women, but not in women without children (p-interaction = 0.03). Associations of density with breast cancer risk did not differ by the levels of BMI, age at menarche, parity, age at first child's birth, age at menopause, alcohol consumption, a family history of breast cancer, a history of benign breast disease, and physical activity. Women with dense breasts, who currently use menopausal hormone therapy are at a particularly high risk of breast cancer. Most breast cancer risk factors do not modify the association between mammographic breast density and breast cancer risk.

Gene variations in oestrogen pathways, CYP19A1, daily 17β-estradiol and mammographic density phenotypes in premenopausal women

INTRODUCTION

High mammographic density is an established breast cancer risk factor, and circulating oestrogen influences oestrogen-regulating gene expression in breast cancer development. However, less is known about the interrelationships of common variants in the CYP19A1 gene, daily levels of oestrogens, mammographic density phenotypes and body mass index (BMI) in premenopausal women.

METHODS

Based on plausible biological mechanisms related to the oestrogen pathway, we investigated the association of single nucleotide polymorphisms (SNPs) in CYP19A1, 17β-estradiol and mammographic density in 202 premenopausal women. DNA was genotyped using the Illumina Golden Gate platform. Daily salivary 17β-estradiol concentrations were measured throughout an entire menstrual cycle. Mammographic density phenotypes were assessed using a computer-assisted method (Madena). We determined associations using multivariable linear and logistic regression models.

RESULTS

The minor alleles of rs749292 were positively (P = 0.026), and the minor alleles of rs7172156 were inversely (P = 0.002) associated with daily 17β-estradiol. We observed an 87% lower level of daily 17β-estradiol throughout a menstrual cycle in heavier women (BMI >23.6 kg/m(2)) of rs7172156 with minor genotype aa compared with major genotype AA. Furthermore, the rs749292 minor alleles were inversely associated with absolute mammographic density (P = 0.032). Lean women with rs749292 minor alleles had 70 to 80% lower risk for high absolute mammographic density (>32.4 cm(2)); Aa: odds ratio (OR) = 0.23 (95% CI 0.07 to 0.75). Lean women with rs7172156 minor homozygous genotype had OR 5.45 for high absolute mammographic density (aa: OR = 5.45 (95% CI 1.13 to 26.3)).

CONCLUSION

Our findings suggest that two SNPs in CYP19A1, rs749292 and rs7172156, are associated with both daily oestrogen levels and mammographic density phenotypes. BMI may modify these associations, but larger studies are needed.

A level set based framework for quantitative evaluation of breast tissue density from MRI data

Breast density is a risk factor associated with the development of breast cancer. Usually, breast density is assessed on two dimensional (2D) mammograms using the American College of Radiology (ACR) classification. Magnetic resonance imaging (MRI) is a non-radiation based examination method, which offers a three dimensional (3D) alternative to classical 2D mammograms. We propose a new framework for automated breast density calculation on MRI data. Our framework consists of three steps. First, a recently developed method for simultaneous intensity inhomogeneity correction and breast tissue and parenchyma segmentation is applied. Second, the obtained breast component is extracted, and the breast-air and breast-body boundaries are refined. Finally, the fibroglandular/parenchymal tissue volume is extracted from the breast volume. The framework was tested on 37 randomly selected MR mammographies. All images were acquired on a 1.5T MR scanner using an axial, T1-weighted time-resolved angiography with stochastic trajectories sequence. The results were compared to manually obtained groundtruth. Dice's Similarity Coefficient (DSC) as well as Bland-Altman plots were used as the main tools for evaluation of similarity between automatic and manual segmentations. The average Dice's Similarity Coefficient values were and for breast and parenchymal volumes, respectively. Bland-Altman plots showed the mean bias () standard deviation equal for breast volumes and for parenchyma volumes. The automated framework produced sufficient results and has the potential to be applied for the analysis of breast volume and breast density of numerous data in clinical and research settings.

What effect does mammographic breast density have on lesion detection in digital mammography?

Effective detection of breast cancer using mammography is an important public health issue worldwide. Breasts that contain higher levels of fibroglandular compared with fatty tissue increase breast radio-opacity making it more difficult to differentiate between normal and abnormal findings. The higher prevalence of breast cancer amongst women with denser breasts demands the origination of effective solutions to manage this common radiographic appearance. This brief review considers the impact of higher levels of density on cancer detection and the importance of digital technology in possibly reducing the negative effects of increased density.

Mammographic density and risk of breast cancer

The radiographic appearance of the breast on mammography varies among women, and reflects variations in breast tissue composition and the different X-ray attenuation characteristics of these tissues. Fat is radiologically lucent and appears dark on a mammogram. Connective and epithelial tissues are radiologically dense and appear light. These variations in appearance are commonly described as the percentage of the breast image that is radiologically dense, or as percent mammographic density (PMD). There is now extensive evidence that PMD is a risk factor for breast cancer, with a 4- to 6-fold gradient in risk between women with 75% or more PMD compared with those with 10% or less. However, the accuracy of risk prediction in individual women is modest. The extent of PMD is associated inversely with greater age, parity, and weight, and is reduced by the menopause and by tamoxifen. PMD is positively associated with greater height, a family history of breast cancer, and is increased by combined hormone therapy. The relative risk associated with density is substantially larger than the relative risk of breast cancer associated with a family history of the disease or any of the menstrual and reproductive risk factors. It is estimated that the risks of breast cancer attributable to density of 50% or more may be 16% for all breast cancers. Although combined hormone therapy and tamoxifen respectively increase a decrease both PMD and breast cancer risk, there is as yet insufficient evidence to use PMD as a surrogate marker for breast cancer.

The relationship between estrogen receptor gene polymorphism and mammographic density in postmenopausal women

OBJECTIVE

To assess the relationship between the presence of PVUII and XBAI polymorphisms in the estrogen receptor α gene and mammographic density in postmenopausal women.

METHODS

For the present analysis, 189 postmenopausal women who had never used hormonal therapy and who did not have clinical or mammographic features were selected. Based on the ACR-BIRADS(®) 2003 classification, the mammographic density was determined by three independent readers (two subjective ratings and one computerized). Blood samples were available to extract DNA according to KIT GFX(®) protocol. PCR-RFLP was then used to identify the polymorphisms.

RESULTS

There was a high degree of agreement among the three readers to determine the mammographic density (κ > 0.75). Sixty women (32%) had dense breasts and 129 (68%) had non-dense breasts. The PVUII polymorphism was found in 132 (69.8%) of 189 women, while the XBAI polymorphism was found in 135 (71.4%) women. Parity (p = 0.02) and body mass index (p < 0.0001) were associated with mammographic density. It was observed that, for the XBAI polymorphism, women with two mutated alleles were approximately 2.5 times more likely to be classified in the dense breasts group (p = 0.003) and the presence of both wild alleles was associated with fibroglandular tissue replacement by fat (p = 0.02).

CONCLUSIONS

There was no significant association of the PVUII polymorphism in the estrogen receptor α gene with mammographic density (p = 0.34). However, the XBAI polymorphism was observed at a higher mutated homozygous frequency in women with dense breasts and there was an increased frequency of wild-type homozygous and heterozygous women with fat-replaced breasts (p = 0.01).

Mammographic density, parity and age at first birth, and risk of breast cancer: an analysis of four case-control studies

Mammographic density is strongly and consistently associated with breast cancer risk. To determine if this association was modified by reproductive factors (parity and age at first birth), data were combined from four case-control studies conducted in the United States and Japan. To overcome the issue of variation in mammographic density assessment among the studies, a single observer re-read all the mammograms using one type of interactive thresholding software. Logistic regression was used to estimate odds ratios (OR) while adjusting for other known breast cancer risk factors. Included were 1,699 breast cancer cases and 2,422 controls, 74% of whom were postmenopausal. A positive association between mammographic density and breast cancer risk was evident in every group defined by parity and age at first birth (OR per doubling of percent mammographic density ranged between 1.20 and 1.39). Nonetheless, the association appeared to be stronger among nulliparous than parous women (OR per doubling of percent mammographic density = 1.39 vs. 1.24; P interaction = 0.054). However, when examined by study location, the effect modification by parity was apparent only in women from Hawaii and when examined by menopausal status, it was apparent in postmenopausal, but not premenopausal, women. Effect modification by parity was not significant in subgroups defined by body mass index or ethnicity. Adjusting for mammographic density did not attenuate the OR for the association between parity and breast cancer risk by more than 16.4%, suggesting that mammographic density explains only a small proportion of the reduction in breast cancer risk associated with parity. In conclusion, this study did not support the hypothesis that parity modifies the breast cancer risk attributed to mammographic density. Even though an effect modification was found in Hawaiian women, no such thing was found in women from the other three locations.

Breast Cancer Risk - Genes, Environment and Clinics

The information available about breast cancer risk factors has increased dramatically during the last 10 years. In particular, studies of low-penetrance genes and mammographic density have improved our understanding of breast cancer risk. In addition, initial steps have been taken in investigating interactions between genes and environmental factors. This review concerns with actual data on this topic. Several genome-wide association studies (GWASs) with a case-control design, as well as large-scale validation studies, have identified and validated more than a dozen single nucleotide polymorphisms (SNPs) associated with breast cancer risk. They are located not only in or close to genes known to be involved in cancer pathogenesis, but also in genes not previously associated with breast cancer pathogenesis, or may even not be related to any genes. SNPs have also been identified that alter the lifetime risk in BRCA mutation carriers. With regard to nongenetic risk factors, studies of postmenopausal hormone replacement therapy (HRT) have revealed important information on how to weigh up the risks and benefits of HRT. Mammographic density (MD) has become an accepted and important breast cancer risk factor. Lifestyle and nutritional considerations have become an integral part of most studies of breast cancer risk, and some improvements have been made in this field as well. More than 10 years after the publication of the first breast cancer prevention studies with tamoxifen, other substances such as raloxifene and aromatase inhibitors have been investigated and have also been shown to have preventive potential. Finally, mammographic screening systems have been implemented in most Western countries during the last decade. These may be developed further by including more individualized methods of predicting the patient's breast cancer risk.

Mammographic density and breast cancer risk: current understanding and future prospects

Variations in percent mammographic density (PMD) reflect variations in the amounts of collagen and number of epithelial and non-epithelial cells in the breast. Extensive PMD is associated with a markedly increased risk of invasive breast cancer. The PMD phenotype is important in the context of breast cancer prevention because extensive PMD is common in the population, is strongly associated with risk of the disease, and, unlike most breast cancer risk factors, can be changed. Work now in progress makes it likely that measurement of PMD will be improved in the near future and that understanding of the genetics and biological basis of the association of PMD with breast cancer risk will also improve. Future prospects for the application of PMD include mammographic screening, risk prediction in individuals, breast cancer prevention research, and clinical decision making.

Breast tissue composition and susceptibility to breast cancer

Breast density, as assessed by mammography, reflects breast tissue composition. Breast epithelium and stroma attenuate x-rays more than fat and thus appear light on mammograms while fat appears dark. In this review, we provide an overview of selected areas of current knowledge about the relationship between breast density and susceptibility to breast cancer. We review the evidence that breast density is a risk factor for breast cancer, the histological and other risk factors that are associated with variations in breast density, and the biological plausibility of the associations with risk of breast cancer. We also discuss the potential for improved risk prediction that might be achieved by using alternative breast imaging methods, such as magnetic resonance or ultrasound. After adjustment for other risk factors, breast density is consistently associated with breast cancer risk, more strongly than most other risk factors for this disease, and extensive breast density may account for a substantial fraction of breast cancer. Breast density is associated with risk of all of the proliferative lesions that are thought to be precursors of breast cancer. Studies of twins have shown that breast density is a highly heritable quantitative trait. Associations between breast density and variations in breast histology, risk of proliferative breast lesions, and risk of breast cancer may be the result of exposures of breast tissue to both mitogens and mutagens. Characterization of breast density by mammography has several limitations, and the uses of breast density in risk prediction and breast cancer prevention may be improved by other methods of imaging, such as magnetic resonance or ultrasound tomography.

[Mammographic density: a valid risk factor for breast cancer?]

Whether estimated or measured, mammographic or breast density, which may be subject to physiological and therapeutic variations, is widely viewed in the literature as an important factor of increased risk for breast cancer. A high breast density, the causes of which are being refined, would increase the relative risk of breast cancer four to six fold, even though some authors direct critics at methodological flaws supporting these results. Three-dimensional imaging will confirm or refute the available results. Meanwhile, radiologists and clinicians must remain vigilant in patients with high breast density.

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.

Mammographic density, breast cancer risk and risk prediction

In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individual's probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models.

Breast density in women with premature ovarian failure using hormone therapy

BACKGROUND

Women with premature ovarian failure (POF) are treated with estrogen-progestin therapy; however, doubts remain regarding the effect of this therapy on the breasts of women with POF.

OBJECTIVE

To evaluate the breast density of women with POF using estrogen-progestin therapy compared with normally menstruating women.

METHODS

A cross-sectional study was performed in 31 women with POF using conjugated equine estrogens and medroxyprogesterone acetate and a control group of 31 normally menstruating women, paired by age. All underwent mammography, analyzed by digitization and Wolfe's classification, the latter defined as non-dense (N1 and P1) or dense (P2 and Dy). Parity, breastfeeding and body mass index were evaluated, as well as duration of hormone use and ovarian failure in the POF group.

RESULTS

Digitization revealed no difference in mean breast density between the groups: 24.1+/-14.6% and 21.8+/-11.3% for POF and control groups, respectively. The Wolfe classification also failed to detect any significant difference between the groups, dense breasts being detected in 51.6% and 35.5% of cases in the POF and control groups, respectively.

CONCLUSION

Periods of hypoestrogenism followed by hormone therapy resulted in no changes in breast density in women with POF, compared with normally menstruating women of the same age.

Preventive oncology in the postmenopausal woman

Breast cancer and endometrial cancer are the most common gynecologic malignancies of the postmenopausal period. As preventive medicine becomes the focus of interest, preventive oncology with special regard to these diseases will undoubtedly become a substantial part of the practicing oncologist's field of duties. The aim of this review is to summarize recommendations dealing with the risk assessment and prevention of breast and endometrial cancer. Obesity, the level of exercise and dietary factors are associated with breast cancer. The selective estrogen receptor modulators tamoxifen and raloxifen have both been shown to decrease the risk to the same extent. Patients at particularly high risk are being detected through the use of the Gail model, a well-known statistical model of risk. Other factors, such as breast density, the serum level of endogenous estrogen and the presence of single nucleotide polymorphisms, have to be taken into consideration.

Genetic susceptibility for breast cancer: How many more genes to be found?

Today, breast cancer is the most commonly occurring cancer among women. It accounts for 22% of all female cancers and the estimated annual incidence of breast cancer worldwide is about one million cases. Many risk factors have been identified but a positive family history remains among the most important ones established for breast cancer, with first-degree relatives of patients having an approximately two-fold elevated risk. It is currently estimated that approximately 20-25% of this risk is explained by known breast cancer susceptibility genes, mostly those conferring high risks, such as BRCA1 and BRCA2. However, these genes explain less than 5% of the total breast cancer incidence, even though several studies have suggested that the proportion of breast cancer that can be attributed to a genetic factor may be as high as 30%. It is thus likely that there are still breast cancer susceptibility genes to be found. It is presently not known how many such genes there still are, nor how many will fall into the class of rare high-risk (e.g. BRCAx) or of common low-risk susceptibility genes, nor if and how these factors interact with each other to cause susceptibility (a polygenic model). In this review we will address this question and discuss the different undertaken approaches used in identifying new breast cancer susceptibility genes, such as (genome-wide) linkage analysis, CGH, LOH, association studies and global gene expression analysis.

Different types of postmenopausal hormone therapy and mammographic density in Norwegian women

Postmenopausal hormone therapy (HT) is associated with increased risk of breast cancer. The HTs used in Scandinavia is associated with higher risk estimates than those used in most other countries. Mammographic density is one of the strongest risk factors for breast cancer, and possibly an intermediate marker for breast cancer. We decided to examine the relationship between use of different types of HT and mammographic density in Norwegian women. Altogether, 1,007 postmenopausal participants in the governmental mammographic screening program were asked about current and previous HT use. Mammograms were classified according to percent and absolute mammographic density. Overall, current users of HT had on average 3.6% higher mean percent mammographic density when compared with never users (p < 0.001). After adjustment for age at screening, number of children and BMI in a multivariate model, women using the continuous estradiol (E(2)) plus norethisterone acetate (NETA) combination had a mean percent mammographic density significantly higher than never users (6.1% absolute difference). Those using the continuous E(2) plus NETA combination had an 4.8% (absolute difference) higher mean percent mammographic density after <5 years of use when compared with never users, while the corresponding number for >or=5 years of use was 7% (p-trend < 0.001). We found similar associations when absolute mammographic density was used as the outcome variable. In summary, our study shows a statistical significant positive dose-response association between current use of the continuous E(2) plus NETA combination and both measures of mammographic density.

Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis

Mammographic features are associated with breast cancer risk, but estimates of the strength of the association vary markedly between studies, and it is uncertain whether the association is modified by other risk factors. We conducted a systematic review and meta-analysis of publications on mammographic patterns in relation to breast cancer risk. Random effects models were used to combine study-specific relative risks. Aggregate data for > 14,000 cases and 226,000 noncases from 42 studies were included. Associations were consistent in studies conducted in the general population but were highly heterogeneous in symptomatic populations. They were much stronger for percentage density than for Wolfe grade or Breast Imaging Reporting and Data System classification and were 20% to 30% stronger in studies of incident than of prevalent cancer. No differences were observed by age/menopausal status at mammography or by ethnicity. For percentage density measured using prediagnostic mammograms, combined relative risks of incident breast cancer in the general population were 1.79 (95% confidence interval, 1.48-2.16), 2.11 (1.70-2.63), 2.92 (2.49-3.42), and 4.64 (3.64-5.91) for categories 5% to 24%, 25% to 49%, 50% to 74%, and > or = 75% relative to < 5%. This association remained strong after excluding cancers diagnosed in the first-year postmammography. This review explains some of the heterogeneity in associations of breast density with breast cancer risk and shows that, in well-conducted studies, this is one of the strongest risk factors for breast cancer. It also refutes the suggestion that the association is an artifact of masking bias or that it is only present in a restricted age range.

Mammographic breast density as an intermediate phenotype for breast cancer

The amount of radiologically dense breast-tissue appearing on a mammogram varies between women because of differences in the composition of breast tissue, and is referred to here as mammographic density. This review presents evidence that mammographic density is a strong risk factor for breast cancer, and that risk of breast cancer is four to five times greater in women with density in more than 75% of the breast than in women with little or no density in the breast. Density in more than 50% of the breast could account for about a third of breast cancers. The epidemiology of mammographic density is consistent with its being a marker of susceptibility to breast cancer. Twin studies have shown that the proportion of the breast occupied by density, at a given age, is highly heritable, and inherited factors explain 63% of the variance. Mammographic breast density has the characteristics of a quantitative trait and might be determined by genes that are easier to identify than those for breast cancer itself. The genes that determine breast density might also be associated with risk of breast cancer, and their identification is also likely to provide insights into the biology of the breast and identify potential targets for preventive strategies.

Percentage density, Wolfe's and Tabár's mammographic patterns: agreement and association with risk factors for breast cancer

Introduction

The purpose of this report was to classify mammograms according to four methods and to examine their agreement and their relationship to selected risk factors for breast cancer.

Method

Mammograms and epidemiological data were collected from 987 women, aged 55 to 71 years, attending the Norwegian Breast Cancer Screening Program. Two readers each classified the mammograms according to a quantitative method (Cumulus or Madena software) and one reader according to two qualitative methods (Wolfe and Tabár patterns). Mammograms classified in the reader-specific upper quartile of percentage density, Wolfe's P2 and DY patterns, or Tabár's IV and V patterns, were categorized as high-risk density patterns and the remaining mammograms as low-risk density patterns. We calculated intra-reader and inter-reader agreement and estimated prevalence odds ratios of having high-risk mammographic density patterns according to selected risk factors for breast cancer.

Results

The Pearson correlation coefficient was 0.86 for the two quantitative density measurements. There was moderate agreement between the Wolfe and Tabár classifications (Kappa = 0.51; 95% confidence interval 0.46 to 0.56). Age at screening, number of children and body mass index (BMI) showed a statistically significant inverse relationship with high-risk density patterns for all four methods (all P < 0.05). After adjustment for percentage density, the Wolfe classification was not associated with any of the risk factors for breast cancer, whereas the association with number of children and BMI remained statistically significant for the Tabár classification. Adjustment for Wolfe or Tabár patterns did not alter the associations between these risk factors and percentage mammographic density.

Conclusion

The four assessments methods seem to capture the same overall associations with risk factors for breast cancer. Our results indicate that the quantitative methods convey additional information over the qualitative methods.

The normal breast and its variations in mammography

The mammographic appearance of the breast varies along the lifetime due to physiological modifications or use of hormonal therapies. Density of the glandular tissue is due to amount of cellular elements of the gland and to hydratation of the tissues. Normal variations are encountered as for example breast asymmetry. The currently breast composition should be described with the BI-RADS lexicon classification. Mammary asymmetry is frequent and has to be differentiated from pathologic changes. A good mammographic technique is mandatory for an adequate visualisation of the breast tissues.

Understanding breast cancer risk -- where do we stand in 2005?

Breast cancer is the most frequent cancer in women and represents the second leading cause of cancer death among women (after lung cancer). The etiology of breast cancer is still poorly understood with known breast cancer risk factors explaining only a small proportion of cases. Risk factors that modulate the development of breast cancer discussed in this review include: age, geographic location (country of origin) and socioeconomic status, reproductive events, exogenous hormones, lifestyle risk factors (alcohol, diet, obesity and physical activity), familial history of breast cancer, mammographic density, history of benign breast disease, ionizing radiation, bone density, height, IGF- 1 and prolactin levels, chemopreventive agents. Additionally, we summarized breast cancer risk associated with the following genetic factors: breast cancer susceptibility high-penetrance genes (BRCA1, BRCA2, p53, PTEN, ATM, NBS1 or LKB1) and low-penetrance genes such as cytochrome P450 genes (CYP1A1, CYP2D6, CYP19), glutathione S-transferase family (GSTM1, GSTP1), alcohol and one-carbon metabolism genes (ADH1C and MTHFR), DNA repair genes (XRCC1, XRCC3, ERCC4/XPF) and genes encoding cell signaling molecules (PR, ER, TNFalpha or HSP70). All these factors contribute to a better understanding of breast cancer risk. Nonetheless, in order to evaluate more accurately the overall risk of breast tumorigenesis, novel genetic and phenotypic traits need to be identified.

The etiology of alcohol-induced breast cancer

Breast cancer is the most common cancer in women in the United States, and it is second among cancer deaths in women. Results of most epidemiologic studies, as well as of most experimental studies in animals, have shown that alcohol intake is associated with increased breast cancer risk. Alcohol consumption may cause breast cancer through different mechanisms, including through mutagenesis by acetaldehyde, through perturbation of estrogen metabolism and response, and by inducing oxidative damage and/or by affecting folate and one-carbon metabolism pathways. Alcohol-metabolizing enzymes are present in human breast tissue. Acetaldehyde is a known, although weak, mutagen. However, results of some studies with human subjects implicate this agent in the context of genetic susceptibilities to increased ethanol metabolism. Reactive oxygen species, resulting from ethanol metabolism, may be involved in breast carcinogenesis by causing damage, as well as by generating DNA and protein adducts. Alcohol interferes with estrogen pathways in multiple ways, influencing hormone levels and effects on the estrogen receptors. With regard to one-carbon metabolism, alcohol can negatively affect folate levels, and the folate perturbation affects DNA methylation and DNA synthesis, which is important in carcinogenesis. Some study results indicate that genetic variants of one-carbon metabolism genes might increase alcohol-related breast cancer risk. For all these pathways, genetic polymorphisms might play a role in increasing further a woman's risk for breast cancer. Additional studies are needed to determine the relative importance of these pathways, as well as the modifying influence by genetic variation.

Interaction of dense breast patterns with other breast cancer risk factors in a case-control study

The question of interactions between breast density and other breast cancer risk factors is of interest, since it bears upon the use of density as a marker for changes in breast cancer risk. We studied breast parenchymal patterns and 13 other potential risk factors for breast cancer in 172 breast cancer cases and 338 age-matched controls in Singapore. Dense breast patterns were defined as having Tabar parenchymal pattern IV or V. We found significant interactions between dense patterns and ethnic group (P=0.046), and between dense patterns and number of deliveries (P=0.04). Among women with nondense breast patterns, the non-Chinese had lower risk than the Chinese with an odds ratio (OR) of 0.47 (95% CI 0.24, 0.88), whereas in those with dense patterns, the non-Chinese had considerably higher risks (OR=5.34, 95% CI 0.54, 52.51). Alternatively expressed, the increased risk with dense patterns was only observed in the non-Chinese (OR=13.99, 95% CI 1.33, 146.99). Among parous women, the protective effect of three or more deliveries was only observed in those with dense breast patterns (OR=0.21, 95% CI 0.06, 0.70). Suggestive but nonsignificant interactions with dense patterns were observed for ever having delivered, age at first delivery, breast feeding and body mass index. The results are consistent with dense breast patterns as a marker for hormonal modification of breast cancer risk.

Mammographic Density in Relation to Daidzein-Metabolizing Phenotypes in Overweight, Postmenopausal Women

Circulating hormones are associated with mammographic density, an intermediate marker of breast cancer risk. Differences in circulating hormones, including estrone and testosterone, have been observed in premenopausal women based on their capacity to metabolize daidzein, an isoflavone found predominantly in soybeans. Equol and O-desmethylangolensin (O-DMA) are products of intestinal bacterial metabolism of daidzein. There is interindividual variability in the capacity to produce daidzein metabolites; individuals can be equol producers or non-producers and O-DMA producers or non-producers. We tested the hypothesis that daidzein-metabolizing phenotypes are associated with mammographic density. Participants were recruited from among 92 sedentary, postmenopausal women, ages 50 to 75 years, who participated in a 1-year physical activity intervention. Pre-intervention mammographic density was determined using a computer-assisted, gray-scale thresholding technique. Fifty-five of these women consumed supplemental soy protein (&gt;10 mg daidzein/d) for 3 days and collected a first-void urine sample on the fourth day to determine daidzein-metabolizing phenotypes. Equol and O-DMA concentrations were measured using gas chromatography-mass spectrometry. Associations between daidzein-metabolizing phenotypes and percent mammographic density were adjusted for age, maximum adult weight, gravidity, family history of breast cancer, and serum follicle-stimulating hormone and free testosterone concentrations. Mammographic density was 39% lower in equol producers compared with non-producers (P = 0.04). O-DMA producers had mammographic density 69% greater than non-producers (P = 0.05). These results suggest that particular intestinal bacterial profiles are associated with postmenopausal mammographic density, and these associations are not entirely explained by differences in reproductive or anthropometric characteristics or circulating hormones.

Can the Gail model be useful in American Indian and Alaska Native populations?

BACKGROUND

Very little is known about breast carcinoma risk factors for American Indian/Alaska Native (AI/AN) women undergoing screening. The Gail model has been a useful tool for predicting the risk of breast carcinoma in several populations. It has not been applied systematically to AI/AN women.

METHODS

The current study was a retrospective review of 1458 screening mammograms performed for AI/AN women. The authors applied the Gail model to estimate both absolute risk and relative risk for breast carcinoma for AI/AN women screened in South Dakota, Arizona, and Alaska.

RESULTS

The mean age of the women was 52.4 years. The onset of menses was not significantly different than expected. The average age at first birth was 20 years, very few women were nulliparous, and few women were age > 30 years at first live birth. The proportion of women reporting a first- or second-degree relative with breast carcinoma was similar to the proportion in the general population. The results of the model indicated an overall average relative risk that ranged from 1.42 to 2.69 compared with white American women, depending on the model assumptions used. Using a modified Gail model and calculating an imputed absolute risk, the expected incidence of breast carcinoma in this population increased to rates of 170-180 per 100,000 in the next 10 years, a significant increase over the Surveillance, Epidemiology and End Results-derived incidence rates from 1988 to 1992 of 31.6 per 100,000 for AI women in New Mexico and 78.9 per 100,000 for AN women.

CONCLUSIONS

The model indicated a likelihood of increasing rates of breast carcinoma in the study population. The data obtained were useful in generating preliminary estimates of breast carcinoma risk in the study population, for which no prospective population survey has been completed. The inherent weaknesses in the current retrospective study indicated the need for a large-scale prospective data collection to confirm these exploratory findings.

Quantitative Assessment of Mammographic Breast Density: Relationship with Breast Cancer Risk

Increased mammographic breast density is a moderate independent risk factor for breast cancer, with findings of published studies in which quantitative methods of assessment were used showing a positive association. Breast density may be quantified by using visual assessment or planimetry. Although the category definitions vary, the odds ratio for developing breast cancer for the most dense compared with the least dense breast tissue categories ranges from 1.8 to 6.0, with most studies yielding an odds ratio of 4.0 or greater. Plausible explanations for the association of breast density with increased breast cancer risk may be the development of premalignant lesions such as atypical ductal hyperplasia, elevated growth factors, or increased estrogen production within the breast due to overactive aromatase. The amount of breast density may be due in part to genetic heredity. However, unlike other risk factors, breast density may be influenced. Specifically, breast density is very hormonally responsive and potentially may be influenced by lifestyle factors such as alcohol intake and diet. Assessment of breast density may become useful in risk assessment and prevention decisions.

Mammographic breast density and family history of breast cancer

The association between mammographic breast density and breast cancer risk may be the result of genetic and/or environmental factors that determine breast density. We reasoned that if the genetic factors that underlie breast density increase breast cancer risk, then breast density should be associated with family history of breast cancer. Therefore, we determined the association between mammographic density and family history of breast cancer among women in the San Francisco Mammography Registry. Mammographic density was classified using the four BI-RADS criteria: 1 = almost entirely fatty, 2 = scattered fibroglandular tissue, 3 = heterogeneously dense, and 4 = extremely dense. We adjusted for age, body mass index, hormone replacement therapy use, menopause status, and personal history of breast cancer. Compared with women with BI-RADS 1 readings, women with higher breast density were more likely to have first-degree relatives with breast cancer (BI-RADS 2, odds ratio [OR] = 1.37, 95% confidence interval [CI] = 0.96 to 1.89; BI-RADS 3, OR = 1.70, 95% CI = 1.19 to 2.40; BI-RADS 4, OR = 1.70, 95% CI = 1.05 to 2.71). Thus, the genetic factors that determine breast density may determine breast cancer risk.

Age as a predictive factor of mammographic breast density in Jamaican women

AIM

We sought to determine the relationship between age, and other clinical characteristics such as parity, oestrogen use, dietary factors and menstrual history on breast density in Jamaican women.

METHODS AND MATERIALS

A retrospective study was done of 891 patients who attended the breast imaging unit. The clinical characteristics were extracted from the patient records. Mammograms were assessed independently by two radiologists who were blinded to the patient clinical characteristics. Breast densities were assigned using the American College of Radiology (ACR) classification.

RESULTS

The concordance between the ACR classification of breast density between the two independent radiologists was 92% with k = 0.76 (SE = 0.02, P < 0.001). Women with low breast density were heavier (81.3 +/- 15.5 kg vs 68.4 +/- 14.3 kg, P < 0.0001, mean +/- standard deviation (SD)) and more obese (body mass index (BMI), 30.3 +/- 5.8 kg m(-2) vs 26.0 +/- 5.2 kg m(-2), P < 0.0001). Mammographic breast density decreased with age. The age adjusted odds ratios (ORs) for predictors significantly related to high breast density were parity, OR = 0.79 (95%CIratio0.71, 0.88), weight, OR = 0.92 (95% CIratio0.91, 0.95), BMI, OR = 0.83 (95% CIratio0.78, 0.89), menopause, OR = 0.51 (95% CIratio0.36, 0.74) and a history of previous breast surgery, OR 1.6 (95% CIratio1.1, 2.3).

CONCLUSION

The rate decline of breast density with age in our population was influenced by parity and body composition.

Mammographic tissue, breast cancer risk, serial image analysis, and digital mammography. Part 1. Tissue and related risk factors

This work is presented as a sequence of two parts. In this leading section, a review of the breast tissue-risk research is provided. Although controversy remains, there is substantial evidence indicating that dense mammographic tissue (a) is a breast cancer risk factor that is at least similar, if not greater, in magnitude with the other known breast cancer risk factors and (b) may be a partial biomarker for some of the other risk factors. Understanding these influences may provide a mechanism for measuring the dynamics of breast cancer risk. The totality of this work is to provide support for an automated serial mammography study under way at the authors' institution, where digital mammographic images are acquired with a full-field digital mammography system. This is a filmless imaging system, where the image is acquired in digital format. This electronic imaging acquisition system provides a prime opportunity to easily couple and manipulate the image data with patient information such as risk probability analysis or other pertinent personal history data for improved automated decision making. In this leading section, the main focus is on understanding elements that will assist in fusing risk probability analysis with automated computer-aided diagnosis. The evidence indicates that there are many factors that influence breast tissue at any given time and thus have the ability to alter the associated radiographic image appearance over time. At the initiation of the serial study it was clear that the authors did not fully understand the nature of the problem: automatically comparing similar mammographic scenes acquired at different times. In the second part of this sequence, the more time-related tissue influences are reviewed.