Mammographic breast density refines Tyrer-Cuzick estimates of breast cancer risk in high-risk women: findings from the placebo arm of the International Breast Cancer Intervention Study I.

INTRODUCTION: Mammographic density is well-established as a risk factor for breast cancer, however, adjustment for age and body mass index (BMI) is vital to its clinical interpretation when assessing individual risk. In this paper we develop a model to adjust mammographic density for age and BMI and show how this adjusted mammographic density measure might be used with existing risk prediction models to identify high-risk women more precisely. METHODS: We explored the association between age, BMI, visually assessed percent dense area and breast cancer risk in a nested case-control study of women from the placebo arm of the International Breast Cancer Intervention Study I (72 cases, 486 controls). Linear regression was used to adjust mammographic density for age and BMI. This adjusted measure was evaluated in a multivariable logistic regression model that included the Tyrer-Cuzick (TC) risk score, which is based on classical breast cancer risk factors. RESULTS: Percent dense area adjusted for age and BMI (the density residual) was a stronger measure of breast cancer risk than unadjusted percent dense area (odds ratio per standard deviation 1.55 versus 1.38; area under the curve (AUC) 0.62 versus 0.59). Furthermore, in this population at increased risk of breast cancer, the density residual added information beyond that obtained from the TC model alone, with the AUC for the model containing both TC risk and density residual being 0.62 compared to 0.51 for the model containing TC risk alone (P =0.002). CONCLUSIONS: In women at high risk of breast cancer, adjusting percent mammographic density for age and BMI provides additional predictive information to the TC risk score, which already incorporates BMI, age, family history and other classic breast cancer risk factors. Furthermore, simple selection criteria can be developed using mammographic density, age and BMI to identify women at increased risk in a clinical setting. CLINICAL TRIAL REGISTRATION NUMBER: http://www.controlled-trials.com/ISRCTN91879928 (Registered: 1 June 2006).

Identification of a novel percent mammographic density locus at 12q24.

Percent mammographic density adjusted for age and body mass index (BMI) is one of the strongest risk factors for breast cancer and has a heritable component that remains largely unidentified. We performed a three-stage genome-wide association study (GWAS) of percent mammographic density to identify novel genetic loci associated with this trait. In stage 1, we combined three GWASs of percent density comprised of 1241 women from studies at the Mayo Clinic and identified the top 48 loci (99 single nucleotide polymorphisms). We attempted replication of these loci in 7018 women from seven additional studies (stage 2). The meta-analysis of stage 1 and 2 data identified a novel locus, rs1265507 on 12q24, associated with percent density, adjusting for age and BMI (P = 4.43 × 10(-8)). We refined the 12q24 locus with 459 additional variants (stage 3) in a combined analysis of all three stages (n = 10 377) and confirmed that rs1265507 has the strongest association in the 12q24 region (P = 1.03 × 10(-8)). Rs1265507 is located between the genes TBX5 and TBX3, which are members of the phylogenetically conserved T-box gene family and encode transcription factors involved in developmental regulation. Understanding the mechanism underlying this association will provide insight into the genetics of breast tissue composition.

Defining the role of PET-CT in staging early breast cancer.

INTRODUCTION: Currently, there is a lack of data on the role of combined positron emission tomography-computed tomography (PET-CT) in the staging of early invasive primary breast cancer. We therefore evaluated the role of (18)F-fluorodeoxyglucose ((18)F-FDG)-PET-CT in this patient population. METHODS: We prospectively recruited 70 consecutive patients (69 women, one man; mean age, 61.9 ± 8.1 years) with early primary breast cancer for staging with (18)F-FDG-PET-CT. All PET-CT images were interpreted by two readers (independently of each other). A third reader adjudicated any discrepancies. All readers had ≥5 years of specific experience. Ethics board approval and informed consent were obtained. RESULTS: The mean clinical follow-up was 22.7 ± 12.6 months. The primary tumor was identified with PET-CT in 64 of 70 patients. Of the unidentified lesions, surgical pathology revealed two intraductal carcinomas, one invasive tubular carcinoma, and three invasive lobular carcinomas. Undiagnosed multifocal breast disease was shown in seven of 70 patients. PET-CT identified avid axillary lymph nodes in 19 of 70 patients, compared with 24 of 70 confirmed during surgery. There were four patients who were axillary node positive on PET but had no axillary disease at surgery. Five patients were reported with avid metastases. Two of those patients were treated for metastatic disease (nodal, lung, and liver in one and bone metastases in the other) following further imaging and clinical assessment. In the other three patients, lesions (lung, n = 1; pleural, n = 1; paratrachael node, n = 1) were subsequently diagnosed as benign lesions. CONCLUSION: Integrated (18)F-FDG-PET-CT may have a role in staging patients presenting with early breast cancer.

The Relationship between Body Mass Index and Mammographic Density during a Premenopausal Weight Loss Intervention Study.

We evaluated the association between short-term change in body mass index (BMI) and breast density during a 1 year weight-loss intervention (Manchester, UK). We included 65 premenopausal women (35-45 years, ≥7 kg adult weight gain, family history of breast cancer). BMI and breast density (semi-automated area-based, automated volume-based) were measured at baseline, 1 year, and 2 years after study entry (1 year post intervention). Cross-sectional (between-women) and short-term change (within-women) associations between BMI and breast density were measured using repeated-measures correlation coefficients and multivariable linear mixed models. BMI was positively correlated with dense volume between-women (r = 0.41, 95%CI: 0.17, 0.61), but less so within-women (r = 0.08, 95%CI: -0.16, 0.28). There was little association with dense area (between-women r = -0.12, 95%CI: -0.38, 0.16; within-women r = 0.01, 95%CI: -0.24, 0.25). BMI and breast fat were positively correlated (volume: between r = 0.77, 95%CI: 0.69, 0.84, within r = 0.58, 95%CI: 0.36, 0.75; area: between r = 0.74, 95%CI: 0.63, 0.82, within r = 0.45, 95%CI: 0.23, 0.63). Multivariable models reported similar associations. Exploratory analysis suggested associations between BMI gain from 20 years and density measures (standard deviation change per +5 kg/m2 BMI: dense area: +0.61 (95%CI: 0.12, 1.09); fat volume: -0.31 (95%CI: -0.62, 0.00)). Short-term BMI change is likely to be positively associated with breast fat, but we found little association with dense tissue, although power was limited by small sample size.

Predicting breast cancer risk using mammographic density measurements from both mammogram sides and views.

Mammographic density is a strong risk factor for breast cancer. Which and how many x-rays are used for research, and how mammographic density is measured varies across studies. In this article, we compared three different measurements (absolute dense area, percent dense area and percent dense volume) from each of four mammograms [left, right, medio-lateral oblique (MLO) and cranio-caudal (CC) views] using three different methods of measurement [computer-assisted thresholding, visual assessment and standard mammogram form (SMF)] to investigate whether additional measurements and/or different methods of measurement provide more information in the prediction of breast cancer risk. Mammographic density was measured in all four mammograms from 318 cases and 899 age-matched controls combined from the Cambridge and Norwich Breast Screening Programmes. Measurements were averaged across various combinations of mammogram type and/or view. Conditional logistic regression was used to estimate odds ratios associated with increasing quintiles of each mammographic measure. Overall, there appeared to be no difference in the fit of the models using two or four mammograms compared to the models using just the contralateral MLO or CC mammogram (all P > 0.07) for all methods of measurement. Common practice of measuring just the contralateral MLO or CC mammogram for analysis in case-control studies investigating the association between mammographic density and breast cancer risk appears to be sufficient.

Assessing the usefulness of a novel MRI-based breast density estimation algorithm in a cohort of women at high genetic risk of breast cancer: the UK MARIBS study.

INTRODUCTION: Mammographic breast density is one of the strongest known risk factors for breast cancer. We present a novel technique for estimating breast density based on 3D T1-weighted Magnetic Resonance Imaging (MRI) and evaluate its performance, including for breast cancer risk prediction, relative to two standard mammographic density-estimation methods. METHODS: The analyses were based on MRI (n = 655) and mammography (n = 607) images obtained in the course of the UK multicentre magnetic resonance imaging breast screening (MARIBS) study of asymptomatic women aged 31 to 49 years who were at high genetic risk of breast cancer. The MRI percent and absolute dense volumes were estimated using our novel algorithm (MRIBview) while mammographic percent and absolute dense area were estimated using the Cumulus thresholding algorithm and also using a 21-point Visual Assessment scale for one medio-lateral oblique image per woman. We assessed the relationships of the MRI and mammographic measures to one another, to standard anthropometric and hormonal factors, to BRCA1/2 genetic status, and to breast cancer risk (60 cases) using linear and Poisson regression. RESULTS: MRI percent dense volume is well correlated with mammographic percent dense area (R = 0.76) but overall gives estimates 8.1 percentage points lower (P < 0.0001). Both show strong associations with established anthropometric and hormonal factors. Mammographic percent dense area, and to a lesser extent MRI percent dense volume were lower in BRCA1 carriers (P = 0.001, P = 0.010 respectively) but there was no association with BRCA2 carrier status. The study was underpowered to detect expected associations between percent density and breast cancer, but women with absolute MRI dense volume in the upper half of the distribution had double the risk of those in the lower half (P = 0.009). CONCLUSIONS: The MRIBview estimates of volumetric breast density are highly correlated with mammographic dense area but are not equivalent measures; the MRI absolute dense volume shows potential as a predictor of breast cancer risk that merits further investigation.

Determinants of percentage and area measures of mammographic density.

Mammographic density is one of the strongest predictors of breast cancer risk. Typically expressed as a percentage of the breast area occupied by radiologically dense tissue on a mammogram, its full value may not be realized because of its negative association with body mass index. A simpler measure of mammographic density, independent of other breast cancer risk factors and equally predictive of risk, would be preferable for risk prediction models. Percentage and area measures of mammographic density were determined for 815 women at high risk for breast cancer from the baseline assessments in the International Breast Cancer Intervention Study I, a trial of tamoxifen for breast cancer prevention conducted between 1992 and 2001. Multivariate linear regression was used to assess associations between risk factors and the mammographic measures. Percent dense area was negatively associated with age, body mass index, menopausal status, predicted risk, and smoking status (R(2) = 24%). Dense area was negatively associated with only age and body mass index (R(2) = 7%), and the latter association was much weaker than for percent dense area. Nondense area was positively associated with age, body mass index, and predicted risk (R(2) = 36%). Dense area was not associated with the multitude of risk factors that percent dense area was, making it a simpler biomarker for risk prediction modeling. Both dense area and percent dense area should be presented whenever possible for comparisons in research.

Cancers in BRCA1 and BRCA2 carriers and in women at high risk for breast cancer: MR imaging and mammographic features.

PURPOSE: To review imaging features of screening-detected cancers on images from diagnostic and prior examinations to identify specific abnormalities to aid earlier detection of or facilitate differentiation of cancers in BRCA1 and BRCA2 carriers and in women with a high risk for breast cancer. MATERIALS AND METHODS: Informed consent and multicenter and local research ethics committee approval were obtained. Women (mean age, 40.1 years; range, 27-55 years) who had at least a 50% risk of being a BRCA1, BRCA2, or TP53 gene mutation carrier were recruited from August 1997 to March 2003 into the United Kingdom Magnetic Resonance Imaging in Breast Screening Study Group trial and were offered annual magnetic resonance (MR) imaging and two-view mammography (total number of screenings, 2065 and 1973; mean, 2.38 and 2.36, respectively). Images in all 39 cancer cases were reread in consensus to document the morphologic and enhancement imaging features on MR and mammographic images in screening and prior examinations. Cases were grouped into genetic subtypes. RESULTS: With MR imaging, there was no difference in morphologic or enhancement characteristics between the genetic subgroups. Cancers on images from prior examinations were of smaller size, showed less enhancement, and were more likely to have a type 1 enhancement curve compared with those cancers in the subsequent diagnostic screening examinations. The tumor sizes detected by using MR imaging and mammography were not significantly different (P = .46). The cancers in BRCA1 carriers found by using MR imaging tended to be smaller than those detected by using mammography (median, 17 mm vs 30 mm; P = .37), whereas the opposite was true for cancers found in BRCA2 carriers (MR imaging median size = 12.5 mm vs mammographic median size = 6 mm; P = .067); the difference was not significant. Tumors with prior MR imaging abnormalities grew at an average of 5.1 mm/y. CONCLUSION: When undertaking MR imaging surveillance in high-risk women, small enhancing lesions should be regarded with suspicion and biopsied or patients should be followed up at 6 months.

Metabolic-flow relationships in primary breast cancer: feasibility of combined PET/dynamic contrast-enhanced CT.

PURPOSE: To assess the feasibility and first experience of combined (18)F-FDG-PET)/dynamic contrast-enhanced (DCE) CT in evaluating breast cancer. METHODS: Nine consecutive female patients (mean age 64.2 years, range 52-74 years) with primary breast carcinoma were prospectively recruited for combined (18)F-FDG PET/DCE-CT. Dynamic CT data were used to calculate a range of parameters of tumour vascularity, and tumour (18)F-FDG uptake (standardized uptake value, SUVmax) was used as a metabolic indicator. RESULTS: One tumour did not enhance and was excluded. The mean tumour SUVmax was 7.7 (range 2.4-26.1). The mean values for tumour perfusion, perfusion normalized to cardiac output, standard perfusion value (SPV) and permeability were 41 ml/min per 100 g (19-59 ml/min per 100 g), 0.56%/100 g (0.33-1.09%/100 g), 3.6 (2.5-5.9) and 0.15/min (0.09-0.30/min), respectively. Linear regression analysis showed a positive correlation between tumour SUV and tumour perfusion normalized to cardiac output (r=0.55, p=0.045) and a marginal correlation between tumour SUV and tumour SPV (r=0.19, p=0.065). There were no significant correlations between tumour SUV and tumour perfusion (r=0.29, p=0.401) or permeability (r=0.03, p=0.682). CONCLUSION: The first data from combined (18)F-FDG-PET/DCE-CT in breast cancer are reported. The technique was successful in eight of nine patients. Breast tumour metabolic and vascular parameters were consistent with previous data from (15)O-H(2)O-PET.

A pilot study of compositional analysis of the breast and estimation of breast mammographic density using three-dimensional T1-weighted magnetic resonance imaging.

PURPOSE: A method and computer tool to estimate percentage magnetic resonance (MR) imaging (MRI) breast density using three-dimensional T(1)-weighted MRI is introduced, and compared with mammographic percentage density [X-ray mammography (XRM)]. MATERIALS AND METHODS: Ethical approval and informed consent were obtained. A method to assess MRI breast density as percentage volume occupied by water-containing tissue on three-dimensional T(1)-weighted MR images is described and applied in a pilot study to 138 subjects who were imaged by both MRI and XRM during the Magnetic Resonance Imaging in Breast Screening study. For comparison, percentage mammographic density was measured from matching XRMs as a ratio of dense to total projection areas scored visually using a 21-point score and measured by applying a two-dimensional interactive program (CUMULUS). The MRI and XRM percent methods were compared, including assessment of left-right and interreader consistency. RESULTS: Percent MRI density correlated strongly (r = 0.78; P < 0.0001) with percent mammographic density estimated using Cumulus. Comparison with visual assessment also showed a strong correlation. The mammographic methods overestimate density compared with MRI volumetric assessment by a factor approaching 2. DISCUSSION: MRI provides direct three-dimensional measurement of the proportion of water-based tissue in the breast. It correlates well with visual and computerized percent mammographic density measurements. This method may have direct application in women having breast cancer screening by breast MRI and may aid in determination of risk.

BRCA1 mutation and young age predict fast breast cancer growth in the Dutch, United Kingdom, and Canadian magnetic resonance imaging screening trials.

PURPOSE: Magnetic resonance imaging (MRI) screening enables early detection of breast cancers in women with an inherited predisposition. Interval cancers occurred in women with a BRCA1 mutation, possibly due to fast tumor growth. We investigated the effect of a BRCA1 or BRCA2 mutation and age on the growth rate of breast cancers, as this may influence the optimal screening frequency. EXPERIMENTAL DESIGN: We reviewed the invasive cancers from the United Kingdom, Dutch, and Canadian MRI screening trials for women at hereditary risk, measuring tumor size at diagnosis and on preceding MRI and/or mammography. We could assess tumor volume doubling time (DT) in 100 cancers. RESULTS: Tumor DT was estimated for 43 women with a BRCA1 mutation, 16 women with a BRCA2 mutation, and 41 women at high risk without an identified mutation. Growth rate slowed continuously with increasing age (P = 0.004). Growth was twice as fast in BRCA1 (P = 0.003) or BRCA2 (P = 0.03) patients as in high-risk patients of the same age. The mean DT for women with BRCA1/2 mutations diagnosed at ages < or =40, 41 to 50, and >50 years was 28, 68, and 81 days, respectively, and 83, 121, and 173 days, respectively, in the high-risk group. Pathologic tumor size decreased with increasing age (P = 0.001). Median size was 15 mm for patients ages < or =40 years compared with 9 mm in older patients (P = 0.003); tumors were largest in young women with BRCA1 mutations. CONCLUSION: Tumors grow quickly in women with BRCA1 mutations and in young women. Age and risk group should be taken into account in screening protocols.

Influence of estrogen receptor alpha and progesterone receptor polymorphisms on the effects of hormone therapy on mammographic density.

Postmenopausal hormone therapy increases mammographic density, a strong breast cancer risk factor, but effects vary across women. We investigated whether the effect of hormone therapy use is modified by polymorphisms in the estrogen receptor (ESR1) and progesterone receptor (PGR) genes in the Dutch Prospect-EPIC and the English EPIC-Norfolk cohorts. Information on hormone therapy use was obtained through questionnaires at recruitment and after 5 years. Blood samples were collected and consecutive mammograms were available through breast cancer screening programs. For 795 hormone therapy users, one mammogram before and a second mammogram during hormone therapy use was included. For 781 never hormone therapy users, mammograms with similar time intervals were included. Mammographic density was assessed using a computer-assisted method. Changes in density were analyzed using linear regression. A statistically significant difference in percentage density change between hormone therapy users and never users was seen in women with the ESR1 PvuII Pp or pp genotype (2.24%; P < 0.01), but not in those with the PP genotype (0.90%; P = 0.47). Similarly, effects of hormone therapy on percentage density were observed in women with the ESR1 XbaI Xx or xx genotype (2.20%; P < 0.01), but not in those with the XX genotype (-0.65%; P = 0.70). Also, effects were seen in women with the PGR +331 GG genotype (2.04%; P < 0.01), but not in those with the GA or AA genotype (0.98%; P = 0.53). The PGR PROGINS polymorphism did not seem to make women more susceptible to the effects of hormone therapy use. In conclusion, our results suggest that specific polymorphisms in the ESR1 and PGR genes may make women more susceptible to the effects of hormone therapy use on mammographic density.

The diagnostic impact of contrast-enhanced MRI in management of breast disease.

For effective use, the diagnostic impact of contrast-enhanced breast MRI (CE MRI) needs to be quantified. This is a retrospective study of 441 women who have undergone CE MRI of the breast from 1 July 1997 to 25 March 2002. Indications for CE MRI studies were diagnostic in 176, monitoring chemotherapy in 126 and study of MRI screening for breast cancer (MARIBS) cases in 139. CE MRI results were confusing or incorrect in 6% of the diagnostic group, 13% of the chemotherapy group and 9% of the MARIBS group. In 18 of 38 of these cases CE MRI stimulated further tests to clarify a clinical query. CE MRI resulted in an increase in confidence or change in clinical plan in 46% of the diagnostic group, 72% of the chemotherapy group and 80% of the MARIBS group. In 44 of 283 of these, CE MRI caused a beneficial change in the clinical plan based on conventional radiology. CE MRI results in a positive diagnostic impact above conventional imaging in a clinically important proportion of patients, but gives some false calls in a smaller proportion (8.6%).

Characterization of pure high-grade DCIS on magnetic resonance imaging using the evolving breast MR lexicon terminology: can it be differentiated from pure invasive disease?

Magnetic resonance imaging (MRI) is now a recognized method of imaging the breast. Unfortunately, there is lack of standardization in the MRI terminology used to characterize the appearance of breast lesions. Moreover, cases of mixed histologies are often imaged. We retrospectively identified cases of pure high-grade ductal carcinoma in situ (DCIS) using the recently introduced breast MRI lexicon and characterized the lesions in order to try and identify features that might distinguish high-grade DCIS from invasive disease. Five-year review of our institution's database revealed 637 patients underwent gadolinium-enhanced breast MRI examination. Twenty patients had histologically proven pure high-grade DCIS. After excluding patients with previous chemotherapy or inadequate MRI examination, 13 patients were analyzed and compared to the 13 most recent cases of pure invasive breast carcinoma. The morphological and dynamic features were then compared. High-grade DCIS cases were significantly more likely to show focal branching pattern (P=.03) and to have an irregular contour (P=.03), compared with invasive disease. Although of marginal statistical significance, DCIS lesions are more likely to have a lower morphological score than invasive carcinoma (P=.06), whilst the latter is more likely to show ring enhancement (P=.07). Use of breast MRI for staging at our institution shows that pure DCIS and pure invasive cancers are both rare entities. Despite the relatively limited numbers, we identified features that would help to differentiate high-grade DCIS from invasive carcinoma on MRI.

Novel Associations between Common Breast Cancer Susceptibility Variants and Risk-Predicting Mammographic Density Measures.

Mammographic density measures adjusted for age and body mass index (BMI) are heritable predictors of breast cancer risk, but few mammographic density-associated genetic variants have been identified. Using data for 10,727 women from two international consortia, we estimated associations between 77 common breast cancer susceptibility variants and absolute dense area, percent dense area and absolute nondense area adjusted for study, age, and BMI using mixed linear modeling. We found strong support for established associations between rs10995190 (in the region of ZNF365), rs2046210 (ESR1), and rs3817198 (LSP1) and adjusted absolute and percent dense areas (all P < 10(-5)). Of 41 recently discovered breast cancer susceptibility variants, associations were found between rs1432679 (EBF1), rs17817449 (MIR1972-2: FTO), rs12710696 (2p24.1), and rs3757318 (ESR1) and adjusted absolute and percent dense areas, respectively. There were associations between rs6001930 (MKL1) and both adjusted absolute dense and nondense areas, and between rs17356907 (NTN4) and adjusted absolute nondense area. Trends in all but two associations were consistent with those for breast cancer risk. Results suggested that 18% of breast cancer susceptibility variants were associated with at least one mammographic density measure. Genetic variants at multiple loci were associated with both breast cancer risk and the mammographic density measures. Further understanding of the underlying mechanisms at these loci could help identify etiologic pathways implicated in how mammographic density predicts breast cancer risk.

Red-clover-derived isoflavones and mammographic breast density: a double-blind, randomized, placebo-controlled trial [ISRCTN42940165].

INTRODUCTION: Isoflavones are hypothesized to protect against breast cancer, but it is not clear whether they act as oestrogens or anti-oestrogens in breast tissue. Our aim was to determine the effects of taking a red clover-derived isoflavone supplement daily for 1 year on mammographic breast density. Effects on oestradiol, follicle-stimulating hormone (FSH), luteinizing hormone (LH), lymphocyte tyrosine kinase activity and menopausal symptoms were also assessed. METHODS: A total of 205 women (age range 49-65 years) with Wolfe P2 or DY mammographic breast patterns were randomly assigned to receive either a red clover-derived isoflavone tablet (26 mg biochanin A, 16 mg formononetin, 1 mg genistein and 0.5 mg daidzein) or placebo. Change in mammographic breast density, serum oestradiol, FSH, LH, menopausal symptoms and lymphocyte tyrosine kinase activity from baseline to 12 months were assessed. RESULTS: A total of 177 women completed the trial. Mammographic breast density decreased in both groups but the difference between the treatment and placebo was not statistically significant. There was a significant interaction between treatment group and oestrogen receptor (ESR1) PvuII polymorphism for the change in estimated percentage breast density (mean +/- standard deviation): TT isoflavone 1.4 +/- 12.3% and TT placebo -9.6 +/- 14.2%; CT isoflavone -5.2 +/- 12.0% and CT placebo -2.8 +/- 10.3%; and CC isoflavone -3.4 +/- 9.7% and CC placebo -1.1 +/- 9.5%. There were no statistically significant treatment effects on oestradiol, FSH, or LH (assessed only in postmenopausal women), or on lymphocyte tyrosine kinase activity. Baseline levels of menopausal symptoms were low, and there were no statistically significant treatment effects on frequency of hot flushes or other menopausal symptoms. CONCLUSION: In contrast to studies showing that conventional hormone replacement therapies increase mammographic breast density, the isoflavone supplement did not increase mammographic breast density in this population of women. Furthermore, there were no effects on oestradiol, gonadotrophins, lymphocyte tyrosine kinase activity, or menopausal symptoms.

What is the recall rate of breast MRI when used for screening asymptomatic women at high risk?

Breast screening acceptability is dependent on sensitivity and recall rate. We aimed to establish the recall rate for MRI and mammography, separately and together, when screening a cohort of women at high genetic risk. Women aged 35-49 years in the MARIBS study form the cohort. We analysed the recall rate, the number of extra tests and their effectiveness. Wilcoxon Rank test was used to estimate the effect of age and logistic regression with robust variance the effect of mammographic density on recall rates. The first 726 screening studies took place in 415 women. Following 86 of these recall occurred, comprising 140 additional investigations. 28 of the cases were resolved without further MRI, and 18 women had more than 2 additional tests. Neither age nor mammographic density was associated with recall. MRI had a recall of rate of 10.19%, and mammography 4.00%. The two techniques largely recalled different cases and 10 cases only (11.62% of those recalled) were abnormal by both tests. The two together had a recall rate of 11.85%. Recall rates varied widely between centres of the study. Breast MRI in asymptomatic high-risk women age 35-49 years largely recalls different women from mammography. The combined figure of approximately 12% may be acceptable for screening and will be useful for planning similar studies.

Mammographic density phenotypes and risk of breast cancer: a meta-analysis.

BACKGROUND: Fibroglandular breast tissue appears dense on mammogram, whereas fat appears nondense. It is unclear whether absolute or percentage dense area more strongly predicts breast cancer risk and whether absolute nondense area is independently associated with risk. METHODS: We conducted a meta-analysis of 13 case-control studies providing results from logistic regressions for associations between one standard deviation (SD) increments in mammographic density phenotypes and breast cancer risk. We used random-effects models to calculate pooled odds ratios and 95% confidence intervals (CIs). All tests were two-sided with P less than .05 considered to be statistically significant. RESULTS: Among premenopausal women (n = 1776 case patients; n = 2834 control subjects), summary odds ratios were 1.37 (95% CI = 1.29 to 1.47) for absolute dense area, 0.78 (95% CI = 0.71 to 0.86) for absolute nondense area, and 1.52 (95% CI = 1.39 to 1.66) for percentage dense area when pooling estimates adjusted for age, body mass index, and parity. Corresponding odds ratios among postmenopausal women (n = 6643 case patients; n = 11187 control subjects) were 1.38 (95% CI = 1.31 to 1.44), 0.79 (95% CI = 0.73 to 0.85), and 1.53 (95% CI = 1.44 to 1.64). After additional adjustment for absolute dense area, associations between absolute nondense area and breast cancer became attenuated or null in several studies and summary odds ratios became 0.82 (95% CI = 0.71 to 0.94; P heterogeneity = .02) for premenopausal and 0.85 (95% CI = 0.75 to 0.96; P heterogeneity < .01) for postmenopausal women. CONCLUSIONS: The results suggest that percentage dense area is a stronger breast cancer risk factor than absolute dense area. Absolute nondense area was inversely associated with breast cancer risk, but it is unclear whether the association is independent of absolute dense area.

The heritability of mammographic breast density and circulating sex-hormone levels: two independent breast cancer risk factors.

BACKGROUND: Mammographic breast density and endogenous sex-hormone levels are both strong risk factors for breast cancer. This study investigated whether there is evidence for a shared genetic basis between these risk factors. METHODS: Using data on 1,286 women from 617 families, we estimated the heritabilities of serum estradiol, testosterone, and sex-hormone binding globulin (SHBG) levels and of three measures of breast density (dense area, nondense area, and percentage density). We tested for associations between hormone levels and density measures and estimated the genetic and environmental correlations between pairs of traits using variance and covariance components models and pedigree-based maximum likelihood methods. RESULTS: We found no significant associations between estradiol, testosterone, or SHBG levels and any of the three density measures, after adjusting for body mass index (BMI). The estimated heritabilities were 63%, 66%, and 65% for square root-transformed adjusted percentage density, dense area, and nondense area, respectively, and 40%, 25%, and 58% for log-transformed-adjusted estradiol, testosterone, and SHBG. We found no evidence of a shared genetic basis between any hormone levels and any measure of density, after adjusting for BMI. The negative genetic correlation between dense and nondense areas remained significant even after adjustment for BMI and other covariates (ρ = -0.34; SE = 0.08; P = 0.0005). CONCLUSIONS: Breast density and sex hormones can be considered as independent sets of traits. IMPACT: Breast density and sex hormones can be used as intermediate phenotypes in the search for breast cancer susceptibility loci.

Mammographic breast density and breast cancer: evidence of a shared genetic basis.

Percent mammographic breast density (PMD) is a strong heritable risk factor for breast cancer. However, the pathways through which this risk is mediated are still unclear. To explore whether PMD and breast cancer have a shared genetic basis, we identified genetic variants most strongly associated with PMD in a published meta-analysis of five genome-wide association studies (GWAS) and used these to construct risk scores for 3,628 breast cancer cases and 5,190 controls from the UK2 GWAS of breast cancer. The signed per-allele effect estimates of single-nucleotide polymorphisms (SNP) were multiplied with the respective allele counts in the individual and summed over all SNPs to derive the risk score for an individual. These scores were included as the exposure variable in a logistic regression model with breast cancer case-control status as the outcome. This analysis was repeated using 10 different cutoff points for the most significant density SNPs (1%-10% representing 5,222-50,899 SNPs). Permutation analysis was also conducted across all 10 cutoff points. The association between risk score and breast cancer was significant for all cutoff points from 3% to 10% of top density SNPs, being most significant for the 6% (2-sided P = 0.002) to 10% (P = 0.001) cutoff points (overall permutation P = 0.003). Women in the top 10% of the risk score distribution had a 31% increased risk of breast cancer [OR = 1.31; 95% confidence interval (CI), 1.08-1.59] compared with women in the bottom 10%. Together, our results show that PMD and breast cancer have a shared genetic basis that is mediated through a large number of common variants.