A Prospective Study of Breast Cancer Risk Using Routine Mammographic Breast Density Measurements

AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial

Screening mammography reduces breast cancer mortality, but studies analyzing interval cancers diagnosed after negative screens have shown that many cancers are missed. Supplemental screening using magnetic resonance imaging (MRI) can reduce the number of missed cancers. However, as qualified MRI staff are lacking, the equipment is expensive to purchase and cost-effectiveness for screening may not be convincing, the utilization of MRI is currently limited. An effective method for triaging individuals to supplemental MRI screening is therefore needed. We conducted a randomized clinical trial, ScreenTrustMRI, using a recently developed artificial intelligence (AI) tool to score each mammogram. We offered trial participation to individuals with a negative screening mammogram and a high AI score (top 6.9%). Upon agreeing to participate, individuals were assigned randomly to one of two groups: those receiving supplemental MRI and those not receiving MRI. The primary endpoint of ScreenTrustMRI is advanced breast cancer defined as either interval cancer, invasive component larger than 15 mm or lymph node positive cancer, based on a 27-month follow-up time from the initial screening. Secondary endpoints, prespecified in the study protocol to be reported before the primary outcome, include cancer detected by supplemental MRI, which is the focus of the current paper. Compared with traditional breast density measures used in a previous clinical trial, the current AI method was nearly four times more efficient in terms of cancers detected per 1,000 MRI examinations (64 versus 16.5). Most additional cancers detected were invasive and several were multifocal, suggesting that their detection was timely. Altogether, our results show that using an AI-based score to select a small proportion (6.9%) of individuals for supplemental MRI after negative mammography detects many missed cancers, making the cost per cancer detected comparable with screening mammography. ClinicalTrials.gov registration: NCT04832594 .

Impact of Mammography Screening Frequency on Breast Cancer Mortality Rates

The frequency of mammography screening remains a topic of ongoing debate. This meta-analysis aimed to investigate the impact of annual vs. biennial mammography screenings on breast cancer mortality rates. A comprehensive search of relevant literature published up to 2021 was performed, with the primary outcome being the difference in breast cancer mortality rates between annual and biennial screenings. The extracted data included relative risks and 95% confidence intervals (CIs), with studies selected based on predetermined inclusion and exclusion criteria, emphasizing the quality of methodology and minimization of bias. Of the included studies, thirteen met the criteria, covering diverse demographic cohorts and screening frequencies. The synthesized data revealed a pattern of lower relative risk in annual screenings compared to biennial screenings across all studies. Notably, subgroup analyses indicated that age and racial background might modulate the effectiveness of screening frequency. In conclusion, this meta-analysis offers strong evidence suggesting that annual mammography screenings could be more effective than biennial screenings in reducing breast cancer mortality rates, especially in certain high-risk demographics. The results emphasize the importance of personalized, evidence-based approaches to mammography, with a call for future research to validate these findings and delve deeper into optimizing breast cancer screening strategies.

Socioeconomic Status at Birth and Breast Tissue Composition in Adolescence and Adulthood

BACKGROUND

Socioeconomic status (SES) at birth is associated with breast cancer risk. Whether this association is driven by changes in breast tissue composition (BTC) prior to adulthood remains unclear.

METHODS

We used multivariable linear regression models to examine whether SES at birth is associated with BTC in adolescence and adulthood using data from a New York City cohort of daughters (n = 165, 11-20 years) and mothers (n = 160, 29-55 years). We used maternal-reported data on daughters' household income and maternal education at birth, analyzed individually and in combination (SES index). Women also reported their own mothers' education at birth. We used optical spectroscopy to evaluate BTC measures that positively (water content, collagen content, optical index) and negatively (lipid content) correlate with mammographic breast density, a recognized breast cancer risk factor.

RESULTS

Being in the highest versus lowest category of the SES index was associated with lower lipid content [βadjusted (βadj) = -0.80; 95% confidence interval (CI), -1.30 to -0.31] and higher collagen content (βadj = 0.54; 95% CI, 0.09-0.99) in adolescence. In women with a body mass index (BMI) <30 kg/m2, higher maternal education at birth (≥ vs. < high school degree) was associated with lower lipid content (βadj = -0.57; 95% CI, -0.97 to -0.17), higher water content (βadj = 0.70; 95% CI, 0.26-1.14), and higher optical index (βadj = 0.53; 95% CI, 0.10-0.95).

CONCLUSIONS

This study supports that SES at birth is associated with BTC in adolescence and adulthood, although the latter association may depend on adult BMI.

IMPACT

Further research is needed to identify the socially patterned early life factors influencing BTC.

A comparison of various methods for measuring breast density and breast tissue composition in adolescent girls and women

This study compared different approaches to measuring breast density and breast tissue composition (BTC) in adolescent girls (n = 42, aged 14-16 years) and their mothers (n = 39, aged 36-61 years) from a cohort in Santiago, Chile. Optical spectroscopy (OS) was used to measure collagen, water, and lipid concentrations, which were combined into a percent breast density index (%BDI). A clinical dual-energy X-ray absorptiometry (DXA) system calibrated to measure breast density provided percent fibroglandular volume (%FGV) from manually delineated images. After digitizing mammogram films, the percent mammographic breast density (%MBD) was measured using computer-assisted software. Partial correlation coefficients (r_partial) were used to evaluate associations between breast density measures and BTC from these three different measurement approaches, adjusting for age and body mass index. %BDI from OS was associated with %FGV from DXA in adolescent girls (r_partial = 0.46, p-value = 0.003), but not in mothers (r_partial = 0.17, p-value = 0.32). In mothers, %FGV from DXA was associated with %MBD from mammograms (r_partial = 0.60, p-value < 0.001). These findings suggest that data from OS, DXA, and mammograms provide related but distinct information about breast density and BTC. Future studies should explore how the information provided by these different devices can be used for breast cancer risk prediction in cohorts of adolescent girls and women.

Exposure to polycyclic aromatic hydrocarbons during pregnancy and breast tissue composition in adolescent daughters and their mothers: a prospective cohort study

BACKGROUND

Polycyclic aromatic hydrocarbons (PAH), which are found in air pollution, have carcinogenic and endocrine disrupting properties that might increase breast cancer risk. PAH exposure might be particularly detrimental during pregnancy, as this is a time when the breast tissue of both the mother and daughter is undergoing structural and functional changes. In this study, we tested the hypothesis that ambient PAH exposure during pregnancy is associated with breast tissue composition, measured one to two decades later, in adolescent daughters and their mothers.

METHODS

We conducted a prospective analysis using data from a New York City cohort of non-Hispanic Black and Hispanic mother-daughter dyads (recruited 1998-2006). During the third trimester of pregnancy, women wore backpacks containing a continuously operating air sampling pump for two consecutive days that measured ambient exposure to eight carcinogenic higher molecular weight nonvolatile PAH compounds (Σ8 PAH) and pyrene. When daughters (n = 186) and mothers (n = 175) reached ages 11-20 and 29-55 years, respectively, optical spectroscopy (OS) was used to evaluate measures of breast tissue composition (BTC) that positively (water content, collagen content, optical index) and negatively (lipid content) correlate with mammographic breast density, a recognized risk factor for breast cancer. Multivariable linear regression was used to evaluate associations between ambient PAH exposure and BTC, overall and by exposure to household tobacco smoke during pregnancy (yes/no). Models were adjusted for race/ethnicity, age, and percent body fat at OS.

RESULTS

No overall associations were found between ambient PAH exposure (Σ8 PAH or pyrene) and BTC, but statistically significant additive interactions between Σ8 PAH and household tobacco smoke exposure were identified for water content and optical index in both daughters and mothers (interaction p values < 0.05). Σ8 PAH exposure was associated with higher water content (β_daughters = 0.42, 95% CI = 0.15-0.68; β_mothers = 0.32, 95% CI = 0.05-0.61) and higher optical index (β_daughters = 0.38, 95% CI = 0.12-0.64; β_mothers = 0.38, 95% CI = 0.12-0.65) in those exposed to household tobacco smoke during pregnancy; no associations were found in non-smoking households (interaction p values < 0.05).

CONCLUSIONS

Exposure to ambient Σ8 PAH and tobacco smoke during pregnancy might interact synergistically to impact BTC in mothers and daughters. If replicated in other cohorts, these findings might have important implications for breast cancer risk across generations.

Mammographic density: intersection of advocacy, science, and clinical practice

Purpose

Here we aim to review the association between mammographic density, collagen structure and breast cancer risk.

Findings

While mammographic density is a strong predictor of breast cancer risk in populations, studies by Boyd show that mammographic density does not predict breast cancer risk in individuals. Mammographic density is affected by age, parity, menopausal status, race/ethnicity, and body mass index (BMI).New studies normalize mammographic density to BMI may provide a more accurate way to compare mammographic density in women of diverse race and ethnicity. Preclinical and tissue-based studies have investigated the role collagen composition and structure in predicting breast cancer risk. There is emerging evidence that collagen structure may activate signaling pathways associated with aggressive breast cancer biology.

Summary

Measurement of film mammographic density does not adequately capture the complex signaling that occurs in women with at-risk collagen. New ways to measure at-risk collagen potentially can provide a more accurate view of risk.

Changes in mammographic density over time and the risk of breast cancer: An observational cohort study

BACKGROUND

The effect of changes in mammographic density over time on the risk of breast cancer remains inconclusive.

METHODS

We used information from four centres of the Breast Cancer Screening Program in Spain in the period 1996-2015. We analysed individual level data from 117,388 women first screened age 50-54, with at least two screening examinations. Breast density was determined using the BI-RADS classification (A to D in increasing order) at earliest and latest screening examination. Adjusted Poisson regression models were used to estimate the relative risk (RR) and 95% confidence intervals (95%CI) of the association between changes in mammographic density and breast cancer risk over time.

RESULTS

During an average 5.8 years of follow-up, 1592 (1.36%) women had a breast cancer diagnosis. An increase in density category increased breast cancer risk, and a decrease in density decreased the risk, compared with women who remained in the same BI-RADS category. Women whose density category increased from B to C or B to D had a RR of 1.55 (95%CI = 1.24-1.94) and 2.32 (95%CI = 1.48-3.63), respectively. The RR for women whose density increased from C to D was 1.51 (95%CI = 1.03-2.22). Changes in BI-RADS density were similarly associated with the risk for invasive cancer than for ductal carcinoma in situ.

CONCLUSIONS

Although a modest proportion of women changed BI-RADS density category, mammographic density changes modulated the risk of breast cancer and identified women at a differential risk. Using two longitudinal measures of BI-RADS density could help target women for risk-based screening strategies.

Convolutional Neural Network Based Breast Cancer Risk Stratification Using a Mammographic Dataset

RATIONALE AND OBJECTIVES

We propose a novel convolutional neural network derived pixel-wise breast cancer risk model using mammographic dataset.

MATERIALS AND METHODS

An institutional review board approved retrospective case-control study of 1474 mammographic images was performed in average risk women. First, 210 patients with new incidence of breast cancer were identified. Mammograms from these patients prior to developing breast cancer were identified and made up the case group [420 bilateral craniocaudal mammograms]. The control group consisted of 527 patients without breast cancer from the same time period. Prior mammograms from these patients made up the control group [1054 bilateral craniocaudal mammograms]. A convolutional neural network (CNN) architecture was designed for pixel-wise breast cancer risk prediction. Briefly, each mammogram was normalized as a map of z-scores and resized to an input image size of 256 × 256. Then a contracting and expanding fully convolutional CNN architecture was composed entirely of 3 × 3 convolutions, a total of four strided convolutions instead of pooling layers, and symmetric residual connections. L2 regularization and augmentation methods were implemented to prevent overfitting. Cases were separated into training (80%) and test sets (20%). A 5-fold cross validation was performed. Software code was written in Python using the TensorFlow module on a Linux workstation with NVIDIA GTX 1070 Pascal GPU.

RESULTS

The average age of patients between the case and the control groups was not statistically different [case: 57.4years (SD, 10.4) and control: 58.2years (SD, 10.9), p = 0.33]. Breast Density (BD) was significantly higher in the case group [2.39 (SD, 0.7)] than the control group [1.98 (SD, 0.75), p < 0.0001]. On multivariate logistic regression analysis, both CNN pixel-wise mammographic risk model and BD were significant independent predictors of breast cancer risk (p < 0.0001). The CNN risk model showed greater predictive potential [OR = 4.42 (95% CI, 3.4-5.7] compared to BD [OR = 1.67 (95% CI, 1.4-1.9). The CNN risk model achieved an overall accuracy of 72% (95%CI, 69.8-74.4) in predicting patients in the case group.

CONCLUSION

Novel pixel-wise mammographic breast evaluation using a CNN architecture can stratify breast cancer risk, independent of the BD. Larger dataset will likely improve our model.

The Dana-Farber CISNET Model for Breast Cancer Screening Strategies: An Update

BACKGROUND

We present updated features to a model developed by Dana-Farber investigators within the Cancer Intervention and Surveillance Modeling Network (CISNET). The initial model was developed to evaluate the impact of mammography screening strategies.

METHODS

This major update includes the incorporation of ductal carcinoma in situ (DCIS) as part of the natural history of breast cancer. The updated model allows DCIS in the pre-clinical state to regress to undetectable early-stage DCIS, or to transition to invasive breast cancer, or to clinical DCIS. We summarize model assumptions for DCIS natural history and model parameters. Another new development is the derivation of analytical expressions for overdiagnosis. Overdiagnosis refers to mammographic identification of breast cancer that would never have resulted in disease symptoms in the patient's remaining lifetime (i.e., lead time longer than residual survival time). This is an inevitable consequence of early detection. Our model uniquely assesses overdiagnosis using an analytical formulation. We derive the lead time distribution resulting from the early detection of invasive breast cancer and DCIS, and formulate the analytical expression for overdiagnosis.

RESULTS

This formulation was applied to assess overdiagnosis from mammography screening. Other model updates involve implementing common model input parameters with updated treatment dissemination and effectiveness, and improved mammography performance. Lastly, the model was expanded to incorporate subgroups by breast density and molecular subtypes.

CONCLUSIONS

The incorporation of DCIS and subgroups and the derivation of an overdiagnosis estimation procedure improve the model for evaluating mammography screening programs.

Adiposity during early adulthood, changes in adiposity during adulthood, attained adiposity, and mammographic density among premenopausal women

PURPOSE

We investigated the associations of adolescent adiposity, changes in adiposity during adulthood, and attained adiposity with volumetric mammographic density measures.

METHODS

We recruited 383 premenopausal women who had a routine screening mammogram at the Breast Health Center, Washington University in St. Louis, MO from December 2015 to October 2016. Trained research personnel assessed current adiposity measures. Weight at ages 18 and 30 were self-reported. We evaluated mammographic density measures: volumetric percent density (VPD), dense volume (DV), and non-dense volume (NDV) using Volpara. Multivariable linear regression models were used to evaluate the associations of adiposity measures with volumetric mammographic density measures.

RESULTS

All attained adiposity measures, BMI at age 18, age 30, and weight change were significantly inversely associated with VPD, and positively associated with DV and NDV. One unit increase in body fat % was associated with a 4.9% decrease in VPD and a 6.5% increase in NDV (p-values <0.001). For each kilogram increase in weight change from age 18 to attained, VPD decreased by 16.3%, 47.1%, and 58.8% for women who gained 5.1-15, 15.1-25 and >25 kg, respectively, compared to women who gained less than 5 kg during this time period (p-values <0.001). Irrespective of BMI at age 18, VPD significantly decreased and NDV increased among women who were currently obese.

CONCLUSIONS

There is a need for mechanistic studies focusing on early adulthood to provide a better understanding of how adiposity in early life relates to mammographic density, and possibly breast cancer development in premenopausal women.

Association between air pollution and mammographic breast density in the Breast Cancer Surveilance Consortium

Background

Mammographic breast density is a well-established strong risk factor for breast cancer. The environmental contributors to geographic variation in breast density in urban and rural areas are poorly understood. We examined the association between breast density and exposure to ambient air pollutants (particulate matter <2.5 μm in diameter (PM_2.5) and ozone (O₃)) in a large population-based screening registry.

Methods

Participants included women undergoing mammography screening at imaging facilities within the Breast Cancer Surveillance Consortium (2001–2009). We included women aged ≥40 years with known residential zip codes before the index mammogram (n = 279,967). Breast density was assessed using the American College of Radiology’s Breast Imaging-Reporting and Data System (BI-RADS) four-category breast density classification. PM_2.5 and O₃ estimates for grids across the USA (2001–2008) were obtained from the US Environmental Protection Agency Hierarchical Bayesian Model (HBM). For the majority of women (94%), these estimates were available for the year preceding the mammogram date. Association between exposure to air pollutants and density was estimated using polytomous logistic regression, adjusting for potential confounders.

Results

Women with extremely dense breasts had higher mean PM_2.5 and lower O₃ exposures than women with fatty breasts (8.97 vs. 8.66 ug/m³ and 33.70 vs. 35.82 parts per billion (ppb), respectively). In regression analysis, women with heterogeneously dense vs. scattered fibroglandular breasts were more likely to have higher exposure to PM_2.5 (fourth vs. first quartile odds ratio (OR) = 1.19, 95% confidence interval (CI) 1.16 − 1.23). Women with extremely dense vs. scattered fibroglandular breasts were less likely to have higher levels of ozone exposure (fourth vs. first quartile OR = 0.80, 95% CI 0.73–0.87).

Conclusion

Exposure to PM_2.5 and O₃ may in part explain geographical variation in mammographic density. Further studies are warranted to determine the causal nature of these associations.

Racial Differences in Quantitative Measures of Area and Volumetric Breast Density

BACKGROUND

Increased breast density is a strong risk factor for breast cancer and also decreases the sensitivity of mammographic screening. The purpose of our study was to compare breast density for black and white women using quantitative measures.

METHODS

Breast density was assessed among 5282 black and 4216 white women screened using digital mammography. Breast Imaging-Reporting and Data System (BI-RADS) density was obtained from radiologists' reports. Quantitative measures for dense area, area percent density (PD), dense volume, and volume percent density were estimated using validated, automated software. Breast density was categorized as dense or nondense based on BI-RADS categories or based on values above and below the median for quantitative measures. Logistic regression was used to estimate the odds of having dense breasts by race, adjusted for age, body mass index (BMI), age at menarche, menopause status, family history of breast or ovarian cancer, parity and age at first birth, and current hormone replacement therapy (HRT) use. All statistical tests were two-sided.

RESULTS

There was a statistically significant interaction of race and BMI on breast density. After accounting for age, BMI, and breast cancer risk factors, black women had statistically significantly greater odds of high breast density across all quantitative measures (eg, PD nonobese odds ratio [OR] = 1.18, 95% confidence interval [CI] = 1.02 to 1.37, P = .03, PD obese OR = 1.26, 95% CI = 1.04 to 1.53, P = .02). There was no statistically significant difference in BI-RADS density by race.

CONCLUSIONS

After accounting for age, BMI, and other risk factors, black women had higher breast density than white women across all quantitative measures previously associated with breast cancer risk. These results may have implications for risk assessment and screening.

Bayesian joint ordinal and survival modeling for breast cancer risk assessment

We propose a joint model to analyze the structure and intensity of the association between longitudinal measurements of an ordinal marker and time to a relevant event. The longitudinal process is defined in terms of a proportional‐odds cumulative logit model. Time‐to‐event is modeled through a left‐truncated proportional‐hazards model, which incorporates information of the longitudinal marker as well as baseline covariates. Both longitudinal and survival processes are connected by means of a common vector of random effects.

General inferences are discussed under the Bayesian approach and include the posterior distribution of the probabilities associated to each longitudinal category and the assessment of the impact of the baseline covariates and the longitudinal marker on the hazard function. The flexibility provided by the joint model makes possible to dynamically estimate individual event‐free probabilities and predict future longitudinal marker values.

The model is applied to the assessment of breast cancer risk in women attending a population‐based screening program. The longitudinal ordinal marker is mammographic breast density measured with the Breast Imaging Reporting and Data System (BI‐RADS) scale in biennial screening exams. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Mammographic density and structural features can individually and jointly contribute to breast cancer risk assessment in mammography screening: a case-control study

Background

Mammographic density is a well-established risk factor for breast cancer. We investigated the association between three different methods of measuring density or parenchymal pattern/texture on digitized film-based mammograms, and examined to what extent textural features independently and jointly with density can improve the ability to identify screening women at increased risk of breast cancer.

Methods

The study included 121 cases and 259 age- and time matched controls based on a cohort of 14,736 women with negative screening mammograms from a population-based screening programme in Denmark in 2007 (followed until 31 December 2010). Mammograms were assessed using the Breast Imaging-Reporting and Data System (BI-RADS) density classification, Tabár’s classification on parenchymal patterns and a fully automated texture quantification technique. The individual and combined association with breast cancer was estimated using binary logistic regression to calculate Odds Ratios (ORs) and the area under the receiver operating characteristic (ROC) curves (AUCs).

Results

Cases showed significantly higher BI-RADS and texture scores on average than controls (p < 0.001). All three methods were individually able to segregate women into different risk groups showing significant ORs for BI-RADS D3 and D4 (OR: 2.37; 1.32–4.25 and 3.93; 1.88–8.20), Tabár’s PIII and PIV (OR: 3.23; 1.20–8.75 and 4.40; 2.31–8.38), and the highest quartile of the texture score (3.04; 1.63–5.67). AUCs for BI-RADS, Tabár and the texture scores (continuous) were 0.63 (0.57–0–69), 0.65 (0.59–0–71) and 0.63 (0.57–0–69), respectively. Combining two or more methods increased model fit in all combinations, demonstrating the highest AUC of 0.69 (0.63-0.74) when all three methods were combined (a significant increase from standard BI-RADS alone).

Conclusion

Our findings suggest that the (relative) amount of fibroglandular tissue (density) and mammographic structural features (texture/parenchymal pattern) jointly can improve risk segregation of screening women, using information already available from normal screening routine, in respect to future personalized screening strategies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-016-2450-7) contains supplementary material, which is available to authorized users.

Synthetic and tissue-derived models for studying rigidity effects on invadopodia activity

Invasion by cancer cells through the extracellular matrix (ECM) of tissues is a critical step in cancer progression and metastasis. Actin-rich subcellular protrusions known as invadopodia are thought to facilitate this process by localizing proteinases which degrade the ECM and allow for cancer cell penetration. We have shown in vitro that invadopodia activity is regulated by the rigidity of the ECM, which suggests that matrix remodeling in vivo may also be regulated by the mechanical properties of tissues. In order to study rigidity effects on invadopodia activity in a controlled manner, we have developed assays in which we have conjugated degradable fluorescent matrix molecules to tunable synthetic substrates. In addition, we have also utilized ex vivo tissue-derived substrates to corroborate our findings. In this chapter, we present detailed protocols describing the synthesis and preparation of our synthetic substrates, polyacrylamide gels and polyurethane elastomers, for use in these matrix degradation assays as well as the steps required to utilize our tissue-derived substrates.

Cancer screenings, diagnostic technology evolution, and cancer control

Screening should allow for the anticipation of cancer diagnosis at an earlier stage, when curative treatment is possible. Screening for cervical, large bowel, and breast cancer were shown to be effective in reducing mortality. The wide acceptance of the screening concept led to the wide diffusion also of screening of uncertain benefit against prostate cancer and skin melanoma. Diagnostic technologies are continuously evolving, and new tests are proposed to improve existing screenings or as screening tests for additional cancer sites (e.g., lung cancer). Cancer screening, however, is a complex and costly intervention that does not result only in benefits but also may cause harm. A major emerging problem of screening is overdiagnosis, or the detection of cases that would have not progressed to the symptomatic phase in the absence of screening. Thus, both experimental and observational evaluation studies are needed to reduce harm caused by screenings and to select effective interventions among many proposed innovations. Finally, the research of markers to assess the aggressive nature of screen-detected lesions is of great importance to improve screenings ' harm/benefit ratio.

Review of the etiology of breast cancer with special attention to organochlorines as potential endocrine disruptors

Breast cancer is the most frequently diagnosed cancer among Canadian women, accounting for about 30% of all new cancer cases each year. Although the incidence of breast cancer has increased over the past 50 years, the cause of this rise is unknown. Risk factors for breast cancer may be classified into four broad categories: (1) genetic/familial, (2) reproductive/hormonal, (3) lifestyle, and (4) environmental. Established risk factors for breast cancer include older age, later age at first full-term pregnancy, no full-term pregnancies, postmenopausal obesity, and genetic factors. However, these known risk factors cannot account for the majority of cases. In the early 1990s, it was suggested that exposure to some environmental chemicals such as organochlorine compounds may play a causal role in the etiology of breast cancer through estrogen-related pathways. The relationship between organochlorines and breast cancer risk has been studied extensively in the past decade and more, and at this point there is no clear evidence to support a causal role of most organochlorine pesticides in the etiology of human breast cancer, but more evidence is needed to assess risk associated with polychlorinated biphenyls (PCBs). Future studies need to consider the combined effects of exposures, concentrate on vulnerable groups such as those with higher levels of exposure, only consider exposures occurring during the most etiologically relevant time periods, and more thoroughly consider gene-environment interactions.

Mammographic Density and Breast Cancer Risk in BRCA1 and BRCA2 Mutation Carriers

High breast density as measured on mammograms is a strong risk factor for breast cancer in the general population, but its effect in carriers of germline BRCA1 and BRCA2 mutations is unclear. We obtained mammograms from 206 female carriers of BRCA1 or BRCA2 mutations, 96 of whom were subsequently diagnosed with breast cancer and 136 relatives of carriers who were themselves noncarriers. We compared the mammographic densities of affected carriers (cases) and unaffected carriers (controls), and of mutation carriers and noncarriers, using a computer-assisted method of measurement and visual assessment by two observers. Analyses were adjusted for age, parity, body mass index, menopausal status, and hormone replacement therapy use. There was no difference in the mean percent density between noncarriers and carriers. Among carriers, increasing mammographic density was associated with an increased risk of breast cancer (P(trend) = 0.024). The odds ratio (OR; 95% confidence interval) for breast cancer associated with a density of > or =50% was 2.29 (1.23-4.26; P = 0.009). The OR did not differ between BRCA1 and BRCA2 carriers or between premenopausal and postmenopausal carriers. The results suggest that the distribution of breast density in BRCA1 and BRCA2 carriers is similar to that in non-carriers. High breast density in carriers is associated with an increased risk of breast cancer, with the relative risk being similar to that observed in the general population. Use of mammographic density could improve individual risk prediction in carriers.