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.

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.

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).

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.

Differences in natural history between breast cancers in BRCA1 and BRCA2 mutation carriers and effects of MRI screening-MRISC, MARIBS, and Canadian studies combined.

BACKGROUND: It is recommended that BRCA1/2 mutation carriers undergo breast cancer screening using MRI because of their very high cancer risk and the high sensitivity of MRI in detecting invasive cancers. Clinical observations suggest important differences in the natural history between breast cancers due to mutations in BRCA1 and BRCA2, potentially requiring different screening guidelines. METHODS: Three studies of mutation carriers using annual MRI and mammography were analyzed. Separate natural history models for BRCA1 and BRCA2 were calibrated to the results of these studies and used to predict the impact of various screening protocols on detection characteristics and mortality. RESULTS: BRCA1/2 mutation carriers (N = 1,275) participated in the studies and 124 cancers (99 invasive) were diagnosed. Cancers detected in BRCA2 mutation carriers were smaller [80% ductal carcinoma in situ (DCIS) or ≤10 mm vs. 49% for BRCA1, P < 0.001]. Below the age of 40, one (invasive) cancer of the 25 screen-detected cancers in BRCA1 mutation carriers was detected by mammography alone, compared with seven (three invasive) of 11 screen-detected cancers in BRCA2 (P < 0.0001). In the model, the preclinical period during which cancer is screen-detectable was 1 to 4 years for BRCA1 and 2 to 7 years for BRCA2. The model predicted breast cancer mortality reductions of 42% to 47% for mammography, 48% to 61% for MRI, and 50% to 62% for combined screening. CONCLUSIONS: Our studies suggest substantial mortality benefits in using MRI to screen BRCA1/2 mutation carriers aged 25 to 60 years but show important clinical differences in natural history. IMPACT: BRCA1 and BRCA2 mutation carriers may benefit from different screening protocols, for example, below the age of 40.

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.

Common breast cancer susceptibility variants in LSP1 and RAD51L1 are associated with mammographic density measures that predict breast cancer risk.

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

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.

Tamoxifen-induced reduction in mammographic density and breast cancer risk reduction: a nested case-control study.

BACKGROUND: Mammographic breast density is a strong risk factor for breast cancer. Tamoxifen, which reduces the risk of breast cancer in women at high risk, also reduces mammographic breast density. However, it is not known if tamoxifen-induced reductions in breast density can be used to identify women who will benefit the most from prophylactic treatment with this drug. METHODS: We conducted a nested case-control study within the first International Breast Cancer Intervention Study, a randomized prevention trial of tamoxifen vs placebo. Mammographic breast density was assessed visually and expressed as a percentage of the total breast area in 5% increments. Case subjects were 123 women diagnosed with breast cancer at or after their first follow-up mammogram, which took place 12-18 months after trial entry, and control subjects were 942 women without breast cancer. Multivariable logistic regression was used to adjust for other risk factors. All statistical tests were two-sided. RESULTS: In the tamoxifen arm, 46% of women had a 10% or greater reduction in breast density at their 12- to 18-month mammogram. Compared with all women in the placebo group, women in the tamoxifen group who experienced a 10% or greater reduction in breast density had 63% reduction in breast cancer risk (odds ratio = 0.37, 95% confidence interval = 0.20 to 0.69, P = .002), whereas those who took tamoxifen but experienced less than a 10% reduction in breast density had no risk reduction (odds ratio = 1.13, 95% confidence interval = 0.72 to 1.77, P = .60). In the placebo arm, there was no statistically significant difference in breast cancer risk between subjects who experienced less than a 10% reduction in mammographic density and subjects who experienced a greater reduction. CONCLUSION: The 12- to 18-month change in mammographic breast density is an excellent predictor of response to tamoxifen in the preventive setting.

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.

Magnetic resonance imaging screening of the contralateral breast in women with newly diagnosed breast cancer: systematic review and meta-analysis of incremental cancer detection and impact on surgical management.

PURPOSE: Preoperative magnetic resonance imaging (MRI) is increasingly used for staging women with breast cancer, including screening for occult contralateral cancer. This article is a review and meta-analysis of studies reporting contralateral MRI in women with newly diagnosed invasive breast cancer. METHODS: We systematically reviewed the evidence on contralateral MRI, calculating pooled estimates for positive predictive value (PPV), true-positive:false-positive ratio (TP:FP), and incremental cancer detection rate (ICDR) over conventional imaging. Random effects logistic regression examined whether estimates were associated with study quality or clinical variables. RESULTS: Twenty-two studies reported contralateral malignancies detected only by MRI in 131 of 3,253 women. Summary estimates were as follows: MRI-detected suspicious findings (TP plus FP), 9.3% (95% CI, 5.8% to 14.7%); ICDR, 4.1% (95% CI, 2.7% to 6.0%), PPV, 47.9% (95% CI, 31.8% to 64.6%); TP:FP ratio, 0.92 (95% CI, 0.47 to 1.82). PPV was associated with the number of test positives and baseline imaging. Few studies included consecutive women, and few ascertained outcomes in all subjects. Where reported, 35.1% of MRI-detected cancers were ductal carcinoma in situ (mean size = 6.9 mm), 64.9% were invasive cancers (mean size = 9.3 mm), and the majority were stage pTis or pT1 and node negative. Effect on treatment was inconsistently reported, but many women underwent contralateral mastectomy. CONCLUSION: MRI detects contralateral lesions in a substantial proportion of women, but does not reliably distinguish benign from malignant findings. Relatively high ICDR may be due to selection bias and/or overdetection. Women must be informed of the uncertain benefit and potential harm, including additional investigations and surgery.

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.

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.

Postmenopausal hormone therapy and changes in mammographic density.

PURPOSE: Hormone therapy (HT) use has been associated with an increased breast cancer risk. We explored the underlying mechanism further by determining the effects of HT on mammographic density, a measure of dense tissue in the breast and a consistent breast cancer risk factor. PATIENTS AND METHODS: A total of 620 HT users and 620 never users from the Dutch Prospect-European Prospective Investigation into Cancer and Nutrition (EPIC) cohort and 175 HT users and 161 never users from the United Kingdom EPIC-Norfolk cohort were included. For HT users, one mammogram before and one mammogram during HT use was included. For never 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. RESULTS: The median time between mammograms was 3.0 years and the median duration of HT use was 1 year. The absolute mean decline in percent density was larger in never users (7.3%) than in estrogen therapy users (6.4%; P = .22) and combined HT users (3.5%; P < .01). The effect of HT appeared to be high in a small number of women, whereas most women were unaffected. CONCLUSION: Our results suggest that HT use, and especially estrogen and progestin use, slows the changes from dense patterns to more fatty patterns that are normally seen in women with increasing age. Given that it is postulated that lifetime cumulative exposure to high density may be related to breast cancer risk, a delay in density decline in HT users potentially could explain their increased breast cancer risk.

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%).

Evaluation of a prospective scoring system designed for a multicenter breast MR imaging screening study.

PURPOSE: To evaluate prospectively the accuracy of a lesion classification system designed for use in a magnetic resonance (MR) imaging high-breast-cancer-risk screening study. MATERIALS AND METHODS: All participating patients provided written informed consent. Ethics committee approval was obtained. The results of 1541 contrast material-enhanced breast MR imaging examinations were analyzed; 1441 screening examinations were performed in 638 women aged 24-51 years at high risk for breast cancer, and 100 examinations were performed in 100 women aged 23-81 years. Lesion analysis was performed in 991 breasts, which were divided into design (491 breasts) and testing (500 breasts) sets. The reference standard was histologic analysis of biopsy samples, fine-needle aspiration cytology, or minimal follow-up of 24 months. The scoring system involved the use of five features: morphology (MOR), pattern of enhancement (POE), percentage of maximal focal enhancement (PMFE), maximal signal intensity-time ratio (MITR), and pattern of contrast material washout (POCW). The system was evaluated by means of (a) assessment of interreader agreement, as expressed in kappa statistics, for 315 breasts in which both readers analyzed the same lesion, (b) assessment of the diagnostic accuracy of the scored components with receiver operating characteristic curve analysis, and (c) logistic regression analysis to determine which components of the scoring system were critical to the final score. A new simplified scoring system developed with the design set was applied to the testing set. RESULTS: There was moderate reader agreement regarding overall lesion outcome (ie, malignant, suspicious, or benign) (kappa=0.58) and less agreement regarding the scored components. The area under the receiver operating characteristic curve (AUC) for the overall lesion score, 0.88, was higher than the AUC for any one component. The components MOR, POE, and POCW yielded the best overall result. PMFE and MITR did not contribute to diagnostic utility. Applying a simplified scoring system to the testing set yielded a nonsignificantly (P=.2) higher AUC than did applying the original scoring system (sensitivity, 84%; specificity, 86.0%). CONCLUSION: Good diagnostic accuracy can be achieved by using simple qualitative descriptors of lesion enhancement, including POCW. In the context of screening, quantitative enhancement parameters appear to be less useful for lesion characterization.