Introduction: Over the last decade several breast cancer risk alleles have been identified which has led to an increased interest in individualised risk prediction for clinical purposes. Methods: We investigate the performance of an up-to-date 18 breast cancer risk single-nucleotide polymorphisms (SNPs), together with mammographic percentage density (PD), body mass index (BMI) and clinical risk factors in predicting absolute risk of breast cancer, empirically, in a well characterised Swedish case-control study of postmenopausal women. We examined the efficiency of various prediction models at a population level for individualised screening by extending a recently proposed analytical approach for estimating number of cases captured. Results: The performance of a risk prediction model based on an initial set of seven breast cancer risk SNPs is improved by additionally including eleven more recently established breast cancer risk SNPs (P = 4.69 × 10^-4). Adding mammographic PD, BMI and all 18 SNPs to a Swedish Gail model improved the discriminatory accuracy (the AUC statistic) from 55% to 62%. The net reclassification improvement was used to assess improvement in classification of women into low, intermediate, and high categories of 5-year risk (P = 8.93 × 10^-9). For scenarios we considered, we estimated that an individualised screening strategy based on risk models incorporating clinical risk factors, mammographic density and SNPs, captures 10% more cases than a screening strategy using the same resources, based on age alone. Estimates of numbers of cases captured by screening stratified by age provide insight into how individualised screening programs might appear in practice. Conclusions: Taken together, genetic risk factors and mammographic density offer moderate improvements to clinical risk factor models for predicting breast cancer.

Introduction: Mammographic density (MD) is a strong, independent risk factor for breast cancer, but measuring MD is time consuming and reader dependent. Objective MD measurement in a high-throughput fashion would enable its wider use as a biomarker for breast cancer. We use a public domain image-processing software for the fully automated analysis of MD and penalized regression to construct a measure that mimics a well-established semiautomated measure (Cumulus). We also describe measures that incorporate additional features of mammographic images for improving the risk associations of MD and breast cancer risk. Methods: We randomly partitioned our dataset into a training set for model building (733 cases, 748 controls) and a test set for model assessment (765 cases, 747 controls). The Pearson product-moment correlation coefficient (r) was used to compare the MD measurements by Cumulus and our automated measure, which mimics Cumulus. The likelihood ratio test was used to validate the performance of logistic regression models for breast cancer risk, which included our measure capturing additional information in mammographic images. Results: We observed a high correlation between the Cumulus measure and our measure mimicking Cumulus (r = 0.884; 95% CI, 0.872 to 0.894) in an external test set. Adding a variable, which includes extra information to percentage density, significantly improved the fit of the logistic regression model of breast cancer risk (P = 0.0002). Conclusions: Our results demonstrate the potential to facilitate the integration of mammographic density measurements into large-scale research studies and subsequently into clinical practice.

Introduction: Mammographic density (MD) is the strongest risk factor for breast cancer. It is also strongly associated with interval cancers (ICs) due to decreased screening sensitivity and possibly by also giving rise to more aggressive tumors. With this information as background, we compared survival in interval and screen-detected cancers, taking MD into consideration. Methods: The patients were postmenopausal women ages 50 to 74 years who were diagnosed with breast cancer in Sweden between 1993 and 1995. A total of 1,115 women with screen-detected cancers and 285 with ICs had available mammograms. Cox proportional hazards models were used to compare breast cancer-specific survival between interval and screen-detected cancers stratified on MD. Results: Hazard rates for breast cancer-specific survival were approximately three times higher in ICs than in screen-detected cancers, independent of MD. After adjustment for tumor size, a proxy for time to diagnosis, ICs in nondense breasts still had a statistically significantly increased hazard rate compared to screen-detected cancers in nondense breasts (5-yr survival hazard ratio (HR) 2.43, P = 0.001). In dense breasts, however, there was no longer evidence of a difference in survival between ICs and screen-detected cancers (5-yr survival HR 1.41, P = 0.486). Conclusions: In nondense breasts, ICs seem to be truly more aggressive than screen-detected cancers. In dense breasts, the poorer prognosis of ICs compared to that of screen-detected cancers may be attributable at least partially to later detection. However, to the best of our knowledge, this study is the first to investigate these relationships, and further studies are warranted to confirm our results.

Introduction: Mammographic density is a strong risk factor for breast cancer, but it is unknown whether density at first breast cancer diagnosis and changes during follow-up influences risk of non-simultaneous contralateral breast cancer (CBC). Methods: We collected mammograms for CBC-patients (cases, N = 211) and unilateral breast cancer patients (controls, N = 211), individually matched on age and calendar period of first breast cancer diagnosis, type of adjuvant therapy and length of follow-up (mean follow-up time: 8.25 years). The odds of CBC as a function of changes of density during follow-up were investigated using conditional logistic regression, adjusting for non-dense area at diagnosis. Results: Patients who experienced ≥10% absolute decrease in percent density had a 55% decreased odds of CBC (OR = 0.45 95% CI: 0.24 to 0.84) relative to patients who had little or no change in density from baseline to first follow-up mammogram (mean = 1.6 (SD = 0.6) years after diagnosis), whereas among those who experienced an absolute increase in percent density we could not detect any effect on the odds of CBC (OR = 0.83 95% CI: 0.24 to 2.87). Conclusion: Decrease of mammographic density within the first two years after first diagnosis is associated with a significantly reduced risk of CBC, this potential new risk predictor can thus contribute to decision-making in follow-up strategies and treatment.

Breast cancer is a complex disease and may be sub-divided into hormone-responsive (estrogen receptor (ER) positive) and non-hormone-responsive subtypes (ER-negative). Some evidence suggests that heterogeneity exists in the associations between coffee consumption and breast cancer risk, according to different estrogen receptor subtypes. We assessed the association between coffee consumption and postmenopausal breast cancer risk in a large population-based study (2,818 cases and 3,111 controls), overall, and stratified by ER tumour subtypes. Odds ratios (OR) and corresponding 95% confidence intervals (CI) were estimated using the multivariate logistic regression models fitted to examine breast cancer risk in a stratified case-control analysis. Heterogeneity among ER subtypes was evaluated in a case-only analysis, by fitting binary logistic regression models, treating ER status as a dependent variable, with coffee consumption included as a covariate. In the Swedish study, coffee consumption was associated with a modest decrease in overall breast cancer risk in the age-adjusted model (OR> 5 cups/day compared to OR≤ 1 cup/day: 0.80, 95% CI: 0.64, 0.99, P trend = 0.028). In the stratified case-control analyses, a significant reduction in the risk of ER-negative breast cancer was observed in heavy coffee drinkers (OR> 5 cups/day compared to OR≤ 1 cup/day : 0.43, 95% CI: 0.25, 0.72, P trend = 0.0003) in a multivariate-adjusted model. The breast cancer risk reduction associated with higher coffee consumption was significantly higher for ER-negative compared to ER-positive tumours (P heterogeneity (age-adjusted) = 0.004). A high daily intake of coffee was found to be associated with a statistically significant decrease in ER-negative breast cancer among postmenopausal women.

Introduction: It is debated whether mammographic density gives rise to more aggressive cancers. We therefore aimed to study the influence of mammographic density on prognosis. Methods: This is a case-only study within a population-based case-control study. Cases were all postmenopausal women in Sweden with incident breast cancer, diagnosed 1993-1995, and aged 50-74 years. Women with pre-diagnostic/diagnostic mammograms were included (n = 1774). Mammographic density of the unaffected breast was assessed using a computer-assisted thresholding technique. The Cox proportional hazards model was used to study recurrence and survival with and without stratification on surgical procedure (breast-conserving surgery vs. mastectomy). Results: Percentage density (PD) was associated with both local and locoregional recurrence even after adjustment for established prognosticators; hazards ratio (HR) 1.92, p = 0.039, for local recurrence and HR 1.67, p = 0.033, for locoregional recurrence for women with PD≥25% compared to PD<25%. Stratification on surgical procedure showed that the associations were also present in mastectomized women. PD was neither associated with distant recurrence nor survival. Conclusions: High mammographic density is an independent risk factor of local and locoregional recurrence but is neither associated with distant metastasis nor survival. The relationships with local and locoregional recurrences were also present in women treated with mastectomy, indicating that they are not merely explained by density masking residual disease in women treated with breast-conserving surgery. Rather there appears to be a true association. Thus, mammographic density should possibly influence adjuvant therapy decisions in the future.