Mammographic density change in a cohort of premenopausal women receiving tamoxifen for breast cancer prevention over 5 years.

BACKGROUND: A decrease in breast density due to tamoxifen preventive therapy might indicate greater benefit from the drug. It is not known whether mammographic density continues to decline after 1 year of therapy, or whether measures of breast density change are sufficiently stable for personalised recommendations. METHODS: Mammographic density was measured annually over up to 5 years in premenopausal women with no previous diagnosis of breast cancer but at increased risk of breast cancer attending a family-history clinic in Manchester, UK (baseline 2010-2013). Tamoxifen (20 mg/day) for prevention was prescribed for up to 5 years in one group; the other group did not receive tamoxifen and were matched by age. Fully automatic methods were used on mammograms over the 5-year follow-up: three area-based measures (NN-VAS, Stratus, Densitas) and one volumetric (Volpara). Additionally, percentage breast density at baseline and first follow-up mammograms was measured visually. The size of density declines at the first follow-up mammogram and thereafter was estimated using a linear mixed model adjusted for age and body mass index. The stability of density change at 1 year was assessed by evaluating mean squared error loss from predictions based on individual or mean density change at 1 year. RESULTS: Analysis used mammograms from 126 healthy premenopausal women before and as they received tamoxifen for prevention (median age 42 years) and 172 matched controls (median age 41 years), with median 3 years follow-up. There was a strong correlation between percentage density measures used on the same mammogram in both the tamoxifen and no tamoxifen groups (all correlation coeficients > 0.8). Tamoxifen reduced mean breast density in year 1 by approximately 17-25% of the inter-quartile range of four automated percentage density measures at baseline, and from year 2, it decreased further by approximately 2-7% per year. Predicting change at 2 years using individual change at 1 year was approximately 60-300% worse than using mean change at 1year. CONCLUSIONS: All measures showed a consistent and large average tamoxifen-induced change in density over the first year, and a continued decline thereafter. However, these measures of density change at 1 year were not stable on an individual basis.

A comparison of five methods of measuring mammographic density: a case-control study.

BACKGROUND: High mammographic density is associated with both risk of cancers being missed at mammography, and increased risk of developing breast cancer. Stratification of breast cancer prevention and screening requires mammographic density measures predictive of cancer. This study compares five mammographic density measures to determine the association with subsequent diagnosis of breast cancer and the presence of breast cancer at screening. METHODS: Women participating in the "Predicting Risk Of Cancer At Screening" (PROCAS) study, a study of cancer risk, completed questionnaires to provide personal information to enable computation of the Tyrer-Cuzick risk score. Mammographic density was assessed by visual analogue scale (VAS), thresholding (Cumulus) and fully-automated methods (Densitas, Quantra, Volpara) in contralateral breasts of 366 women with unilateral breast cancer (cases) detected at screening on entry to the study (Cumulus 311/366) and in 338 women with cancer detected subsequently. Three controls per case were matched using age, body mass index category, hormone replacement therapy use and menopausal status. Odds ratios (OR) between the highest and lowest quintile, based on the density distribution in controls, for each density measure were estimated by conditional logistic regression, adjusting for classic risk factors. RESULTS: The strongest predictor of screen-detected cancer at study entry was VAS, OR 4.37 (95% CI 2.72-7.03) in the highest vs lowest quintile of percent density after adjustment for classical risk factors. Volpara, Densitas and Cumulus gave ORs for the highest vs lowest quintile of 2.42 (95% CI 1.56-3.78), 2.17 (95% CI 1.41-3.33) and 2.12 (95% CI 1.30-3.45), respectively. Quantra was not significantly associated with breast cancer (OR 1.02, 95% CI 0.67-1.54). Similar results were found for subsequent cancers, with ORs of 4.48 (95% CI 2.79-7.18), 2.87 (95% CI 1.77-4.64) and 2.34 (95% CI 1.50-3.68) in highest vs lowest quintiles of VAS, Volpara and Densitas, respectively. Quantra gave an OR in the highest vs lowest quintile of 1.32 (95% CI 0.85-2.05). CONCLUSIONS: Visual density assessment demonstrated a strong relationship with cancer, despite known inter-observer variability; however, it is impractical for population-based screening. Percentage density measured by Volpara and Densitas also had a strong association with breast cancer risk, amongst the automated measures evaluated, providing practical automated methods for risk stratification.

A case-control study to assess the impact of mammographic density on breast cancer risk in women aged 40-49 at intermediate familial risk.

Mammographic density is a strong risk factor for breast cancer, but its potential application in risk management is not clear, partly due to uncertainties about its interaction with other breast cancer risk factors. We aimed to quantify the impact of mammographic density on breast cancer risk in women aged 40-49 at intermediate familial risk of breast cancer (average lifetime risk of 23%), in particular in premenopausal women, and to investigate its relationship with other breast cancer risk factors in this population. We present the results from a case-control study nested with the FH01 cohort study of 6,710 women mostly aged 40-49 at intermediate familial risk of breast cancer. One hundred and three cases of breast cancer were age-matched to one or two controls. Density was measured by semiautomated interactive thresholding. Absolute density, but not percent density, was a significant risk factor for breast cancer in this population after adjusting for area of nondense tissue (OR per 10 cm(2) = 1.07, 95% CI 1.00-1.15, p = 0.04). The effect was stronger in premenopausal women, who made up the majority of the study population. Absolute density remained a significant predictor of breast cancer risk after adjusting for age at menarche, age at first live birth, parity, past or present hormone replacement therapy, and the Tyrer-Cuzick 10-year relative risk estimate of breast cancer. Absolute density can improve breast cancer risk stratification and delineation of high-risk groups alongside the Tyrer-Cuzick 10-year relative risk estimate.

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.

Using mammographic density to predict breast cancer risk: dense area or percentage dense area.

INTRODUCTION: Mammographic density (MD) is one of the strongest risk factors for breast cancer. It is not clear whether this association is best expressed in terms of absolute dense area or percentage dense area (PDA). METHODS: We measured MD, including nondense area (here a surrogate for weight), in the mediolateral oblique (MLO) mammogram using a computer-assisted thresholding technique for 634 cases and 1,880 age-matched controls from the Cambridge and Norwich Breast Screening programs. Conditional logistic regression was used to estimate the risk of breast cancer, and fits of the models were compared using likelihood ratio tests and the Bayesian information criteria (BIC). All P values were two-sided. RESULTS: Square-root dense area was the best single predictor (for example, χ1² = 53.2 versus 44.4 for PDA). Addition of PDA and/or square-root nondense area did not improve the fit (both P > 0.3). Addition of nondense area improved the fit of the model with PDA (χ1² = 11.6; P < 0.001). According to the BIC, the PDA and nondense area model did not provide a better fit than the dense area alone model. The fitted values of the two models were highly correlated (r = 0.97). When a measure of body size is included with PDA, the predicted risk is almost identical to that from fitting dense area alone. CONCLUSIONS: As a single parameter, dense area provides more information than PDA on breast cancer risk.

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.

Mammographic density, estrogen receptor status and other breast cancer tumor characteristics.

The aim of this study was to examine the relationship between mammographic density and histological characteristics of breast tumors within a case-control study population. This study was an expansion of a large size case-control study examining the relationship between breast density and breast cancer risk. Percent and area of breast density was assessed in 370 invasive breast cancer cases and 1904 age-matched controls, using a computer-assisted method. Associations between breast density and estrogen receptor (ER) status, histological grade, histological size, lymph node status, vascular invasion, disease extent, and Nottingham Prognostic Index were evaluated, using logistic regression. Women with 50% or greater mammographic density have a 2.63-fold risk (95% confidence interval [95% CI] = 1.78-3.87; p < 0.001) of developing breast cancer compared to women with less than 10% density. Increase in every category of percentage of breast density is also associated with a 1.45-fold risk in developing ER positive tumors relative to ER negative tumors (odds ratio [OR] = 1.02; 95% CI = 1.00-1.04; p = 0.048), and increase in every quartile of absolute area of density is associated with a 1.48-fold ER positive breast cancer risk [95% CI = 1.06-2.07; p = 0.020]. Furthermore, breast density was found to be associated with specifically ER positivity, invasion as well as invasion with in situ, histological grades 1 and 2, tumor size larger than 1.1 cm, lack of vascular invasion, lymph node positivity and negativity, and NPI less than 4.0. After stratifying the data according to mode of diagnosis, the relationship became slightly stronger in the interval cancer group. Similar results were in observed using percent density and absolute density readings. Mammographic density was a stronger risk factor for ER positive [OR = 2.94; 95% CI = 1.94-4.43; p < 0.001] than ER negative cancers when comparing breasts with greater than 50% dense region to those with less than 10% density. No other tumor characteristic had a significant correlation with breast density. These results suggest that mammographic percent density may be more strongly related to ER positive than ER negative breast cancer, but otherwise is a risk factor for breast cancer independent of other tumor characteristics.

Care of Infants in the Past: Bridging evolutionary anthropological and bioarchaeological approaches.

The importance of care of infants and children in palaeoanthropological and human behavioural ecological research on the evolution of our species is evident in the diversity of research on human development, alloparental care, and learning and social interaction. There has been a recent surge of interest in modelling the social implications of care provision for people with serious disabilities in bioarchaeology. However, there is a lack of acknowledgement of infant and child care in bioarchaeology, despite the significant labour and resources that are required, and the implications this has for health outcomes within societies. Drawing on the recent proliferation of studies on infancy and childhood in evolutionary anthropology and bioarchaeology, this paper presents ways the subdisciplines may draw on research developments from each field to advance a more holistic understanding of the evolutionary, social and health significance of infant and children care in the past.

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.

Genetic models for the familial aggregation of mammographic breast density.

BACKGROUND: Mammographic breast density (MBD) has a strong genetic component. Investigating the genetic models for mammographic density may provide further insights into the genetic factors affecting breast cancer risk. PURPOSE: To evaluate the familial aggregation of MBD and investigate the genetic models of susceptibility. METHODS: We used data on 746 women from 305 families participating in the Sisters in Breast Screening study. Retrieved mammograms were digitized, and percent mammographic density was determined using the Cumulus software. Linear regression analysis was done to identify the factors that are associated with mammographic density and a multivariate regression model was constructed. Familial correlations between relative pairs were calculated using the residuals from these models. Genetic models of susceptibility were investigated using segregation analysis. RESULTS: After adjusting for covariates, the intraclass correlation coefficient among the residuals was 0.26 (95% confidence interval, 0.16-0.36) in sister-sister pairs and 0.67 (0.27-1.00) among the monozygotic twin pairs. The most parsimonious model was a Mendelian single major gene model in which an allele with population frequency 0.39 (95% confidence interval, 0.33-0.46) influenced mammographic density in an additive fashion. This model explained 66% of the residual variance. CONCLUSION: These results confirm that MBD has a strong heritable basis, and suggest that major genes may explain some of the familial aggregation. These results may have implications for the search of genes that control mammographic density.

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.

Technical aspects of breast MRI--do they affect outcomes?

In a systematic review of breast MRI for assessing ipsilateral breast cancer to detect additional lesions, technical details were extracted from publications to assess their effect on diagnostic performance. Where technical parameters were complete, we examined their effect on summary ROC models, and the TP:FP ratio and PPV, using random-effects logistic regression. A total of 2,801 breasts in 19 publications underwent statistical analysis for year of study, slice thickness, and repetitions after contrast-medium injection. None were associated with TP/FP ratio. Summary ROC analysis provided weak evidence (P = 0.09) of an association between diagnostic performance and time period, however no trend over time. Tesla strength was reported in 2,801 cases. Other key information was omitted including whether both breasts were examined for 1683 (60%), position of the patient in 1,375 (49%), and imaging planes used in 688 (25%). Contrast agent and dose were reported for 2,646 (95%) breasts. Reporting technique was inconsistently reported. Single radiology reports were found in 1,637 (58%) cases, double in 347 (12.4%), and in 960 (34%) knowledge of mammography or ultrasound findings was not stated. Slice thickness, number of sequences after contrast medium, and year of study did not show significant performance differences. Other technical information was deficient. There is an urgent need to improve the quality of reporting of breast MRI studies.

Physical activity and mammographic breast density in the EPIC-Norfolk cohort study.

Physical inactivity and high mammographic breast density have both been associated with increased breast cancer risk. However, the association between physical activity and mammographic breast density remains inconsistent. In the European Prospective Investigation of Cancer (EPIC)-Norfolk population-based cohort study (United Kingdom), the authors investigated the cross-sectional association between physical activity level at baseline during 1993-1997 and breast density among 1,394 postmenopausal, cancer-free women. Usual physical activity was assessed by a brief, validated questionnaire. Percentage breast density was determined visually from mammograms by three trained radiologists using the Boyd six-category scale. The association between physical activity level and breast density risk category was examined. No statistically significant association between physical activity and percentage breast density was observed in the unadjusted or adjusted regression models. A suggested increase in breast density for the most active women in the unadjusted regression analysis (odds ratio = 1.13, 95% confidence interval: 0.71, 1.80) was reversed after inclusion of body mass index and reproductive and lifestyle variables (odds ratio = 0.78, 95% confidence interval: 0.45, 1.34). The lack of an association between physical activity and percentage breast density suggests that an association between physical activity and breast cancer risk is unlikely to be mediated through an effect on mammographic breast density.

Evaluating the effectiveness of using standard mammogram form to predict breast cancer risk: case-control study.

Breast density is a well-known breast cancer risk factor. Most current methods of measuring breast density are area based and subjective. Standard mammogram form (SMF) is a computer program using a volumetric approach to estimate the percent density in the breast. The aim of this study is to evaluate the current implementation of SMF as a predictor of breast cancer risk by comparing it with other widely used density measurement methods. The case-control study comprised 634 cancers with 1,880 age-matched controls combined from the Cambridge and Norwich Breast Screening Programs. Data collection involved assessing the films based both on Wolfe's parenchymal patterns and on visual estimation of percent density and then digitizing the films for computer analysis (interactive threshold technique and SMF). Logistic regression was used to produce odds ratios associated with increasing categories of breast density. Density measures from all four methods were strongly associated with breast cancer risk in the overall population. The stepwise rises in risk associated with increasing density as measured by the threshold method were 1.37 [95% confidence interval (95% CI), 1.03-1.82], 1.80 (95% CI, 1.36-2.37), and 2.45 (95% CI, 1.86-3.23). For each increasing quartile of SMF density measures, the risks were 1.11 (95% CI, 0.85-1.46), 1.31 (95% CI, 1.00-1.71), and 1.92 (95% CI, 1.47-2.51). After the model was adjusted for SMF results, the threshold readings maintained the same strong stepwise increase in density-risk relationship. On the contrary, once the model was adjusted for threshold readings, SMF outcome was no longer related to cancer risk. The available implementation of SMF is not a better cancer risk predictor compared with the thresholding method.

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.