Purpose: Tamoxifen treatment is associated with a reduction in mammographic density and an improved survival. However, the extent to which change in mammographic density during adjuvant tamoxifen therapy can be used to measure response to treatment is unknown.Patients and Methods: Overall, 974 postmenopausal patients with breast cancer who had both a baseline and a follow-up mammogram were eligible for analysis. On the basis of treatment information abstracted from medical records, 474 patients received tamoxifen treatment and 500 did not. Mammographic density was measured by using an automated thresholding method and expressed as absolute dense area. Change in mammographic density was calculated as percentage change from baseline. Survival analysis was performed by using delayed-entry Cox proportional hazards regression models, with death as a result of breast cancer as the end point. Analyses were adjusted for a range of patient and tumor characteristics.Results: During a 15-year follow-up, 121 patients (12.4%) died from breast cancer. Women treated with tamoxifen who experienced a relative density reduction of more than 20% between baseline and first follow-up mammogram had a reduced risk of death as a result of breast cancer of 50% (hazard ratio, 0.50; 95% CI, 0.27 to 0.93) compared with women with stable mammographic density. In the no-tamoxifen group, there was no statistically significant association between mammographic density change and survival. The survival advantage was not observed when absolute dense areas at baseline or follow-up were evaluated separately.Conclusion: A decrease in mammographic density after breast cancer diagnosis appears to serve as a prognostic marker for improved long-term survival in patients receiving adjuvant tamoxifen, and these data should be externally validated.

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: Breast cancers of different histology have different clinical and prognostic features. There are also indications of differences in aetiology. We therefore evaluated the risk of the three most common histological subtypes in relation to menopausal hormone therapy and other breast cancer risk factors. Methods: We used a population-based case-control study of breast cancer to evaluate menopausal hormone therapy and other breast cancer risk factors for risk by histological subtype. Women aged 50 to 74 years, diagnosed with invasive ductal (n = 1,888), lobular (n = 308) or tubular (n = 93) breast cancer in Sweden in 1993 to 1995 were compared with 3,065 age-frequency matched controls randomly selected from the population. Unconditional logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for ductal, lobular, and tubular cancer. Results: Women who had used medium potency estrogen alone were at increased risks of both ductal and lobular cancer. Medium potency estrogen-progestin was associated with increased risks for all subtypes, but the estimates for lobular and tubular cancer were higher compared with ductal cancer. We found OR 5.6 (95% CI 3.2–9.7) for lobular cancer, OR 6.5 (95% CI 2.8–14.9) for tubular cancer and OR 2.3 (95% CI 1.6–3.3) for ductal cancer with ≥5 years use of medium potency estrogen-progestin therapy. Low potency oral estrogen (mainly estriol) appeared to be associated with an increased risk for lobular cancer, but the association was strongest for short-term use. Reproductive and anthropometric factors, smoking, and past use of oral contraceptives were mostly similarly related to the risks of the three breast cancer subtypes. Recent alcohol consumption of > 10 g alcohol/day was associated with increased risk only for tubular cancer (OR 3.1, 95% CI 1.4–6.8). Conclusion: Menopausal hormone therapy was associated with increased risks for breast cancer of both ductal and lobular subtype, and medium potency estrogen-progestin therapy was more strongly associated with lobular compared with ductal cancer. We also found medium potency estrogen-progestin therapy and alcohol to be strongly associated with tubular cancer. With some exceptions, most other risk factors seemed to be similarly associated with the three subtypes of breast cancer.