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Objectives: The association between breast cancer (BC) and thyroid disorders has been widely explored with unclear results. Mammographic density (MD) is one of the strongest risk factor for BC. This study explores the relationship between thyroid diseases and MD in Spanish women. Materials & methods: This cross-sectional study covered 2883 women aged 47-71 years participating in 7 BC screening programs in 2010. They allowed access to their mammograms, had anthropometrical-measures taken, and answered a telephonic epidemiological interview which included specific questions on thyroid diseases. Percentage of MD was assessed with a semiautomatic-computer tool (DM-scan) by two trained radiologists. We calculated the geometric mean of MD percentages (mean MD). Multivariable mixed linear regression models with random screening-center-specific intercepts were fitted, using log-transformed percentage of MD as dependent variable and adjusting for age, body mass index, menopausal status and other confounders. e(beta) represents the relative increase of mean MD. Results: 13.9% of the participants reported personal history of thyroid disease. MD was not associated to hyperthyroidism (e(beta): 1.05, 95%CI: 0.82-1.36), hypothyroidism (e(beta):1.02, 95%CI: 0.75-1.38), thyroid nodules (e(beta):1.01, 95%CI: 0.85-1.19) or thyroid cancer (e(beta):1.03, 95%CI: 0.56-1.92). However, women with goiter had lower MD (mean MDno-goiter: 13.4% vs mean MDgoiter: 10.6%; e(beta):0.79, 95%CI: 0.64-0.98) and those with Hashimoto thyroiditis had higher MD (mean MDno-thyroiditis: 13.3% vs mean MDthyroidits: 25.8%; e(beta) 95%CI: 1.00-3.77). Conclusion: Functional thyroid disorders were not related to MD. However, MD was lower in women with goiter and higher in those reporting Hashimoto's thyroiditis. These relationships should be confirmed in future studies. (C) 2017 Elsevier Ltd. All rights reserved.

Background: Night-shift work (NSW) has been suggested as a possible cause of breast cancer, and its association with mammographic density (MD), one of the strongest risk factors for breast cancer, has been scarcely addressed. This study examined NSW and MD in Spanish women. Methods: The study covered 2,752 women aged 45-68 years recruited in 2007-2008 in 7 population-based public breast cancer screening centers, which included 243 women who had performed NSW for at least one year. Occupational data and information on potential confounders were collected by personal interview. Two trained radiologist estimated the percentage of MD assisted by a validated semiautomatic computer tool (DM-scan). Multivariable mixed linear regression models with random screening center-specific intercepts were fitted using log-transformed percentage of MD as the dependent variable and adjusting by known confounding variables. Results: Having ever worked in NSW was not associated with MD [e(beta):0.96; 95% confidence interval (CI), 0.86-1.06]. However, the adjusted geometric mean of the percentage of MD in women with NSW for more than 15 years was 25% higher than that of those without NSW history (MD>15 (years):20.7% vs. MDnever:16.5%; e(beta):1.25; 95% CI, 1.01-1.54). This association was mainly observed in postmenopausal participants (e(beta):1.28; 95% CI, 1.00-1.64). Among NSW-exposed women, those with

The task of breast density quantification is becoming increasingly relevant due to its association with breast cancer risk. In this work, a semi-automated and a fully automated tools to assess breast density from full-field digitized mammograms are presented. The first tool is based on a supervised interactive thresholding procedure for segmenting dense from fatty tissue and is used with a twofold goal: for assessing mammographic density(MD) in a more objective and accurate way than via visual-based methods and for labeling the mammograms that are later employed to train the fully automated tool. Although most automated methods rely on supervised approaches based on a global labeling of the mammogram, the proposed method relies on pixel-level labeling, allowing better tissue classification and density measurement on a continuous scale. The fully automated method presented combines a classification scheme based on local features and thresholding operations that improve the performance of the classifier. A dataset of 655 mammograms was used to test the concordance of both approaches in measuring MD. Three expert radiologists measured MD in each of the mammograms using the semi-automated tool (DM-Scan). It was then measured by the fully automated system and the correlation between both methods was computed. The relation between MD and breast cancer was then analyzed using a case-control dataset consisting of 230 mammograms. The Intraclass Correlation Coefficient (ICC) was used to compute reliability among raters and between techniques. The results obtained showed an average ICC = 0.922 among raters when using the semi-automated tool, whilst the average correlation between the semi-automated and automated measures was ICC = 0.838. In the case-control study, the results obtained showed Odds Ratios (OR) of 1.38 and 1.50 per 10% increase in MD when using the semi-automated and fully automated approaches respectively. It can therefore be concluded that the automated and semi-automated MD assessments present a good correlation. Both the methods also found an association between MD and breast cancer risk, which warrants the proposed tools for breast cancer risk prediction and clinical decision making. A full version of the DM-Scan is freely available. (C) 2014 Elsevier Ireland Ltd. All rights reserved.

High mammographic density (MD) is a phenotype risk marker for breast cancer. Body mass index (BMI) is inversely associated with MD, with the breast being a fat storage site. We investigated the influence of abdominal fat distribution and adult weight gain on MD, taking age, BMI and other confounders into account. Because visceral adiposity and BMI are associated with breast cancer only after menopause, differences in pre- and post-menopausal women were also explored. We recruited 3,584 women aged 45-68 years within the Spanish breast cancer screening network. Demographic, reproductive, family and personal history data were collected by purpose-trained staff, who measured current weight, height, waist and hip circumferences under the same protocol and with the same tools. MD was assessed in the left craniocaudal view using Boyd's Semiquantitative Scale. Association between waist-to-hip ratio, adult weight gain (difference between current weight and self-reported weight at 18 years) and MD was quantified by ordinal logistic regression, with random center-specific intercepts. Models were adjusted for age, BMI, breast size, time since menopause, parity, family history of breast cancer and hormonal replacement therapy use. Natural splines were used to describe the shape of the relationship between these two variables and MD. Waist-to-hip ratio was inversely associated with MD, and the effect was more pronounced in pre-menopausal (OR = 0.53 per 0.1 units; 95 % CI = 0.42-0.66) than in post-menopausal women (OR = 0.73; 95 % CI = 0.65-0.82) (P of heterogeneity = 0.010). In contrast, adult weight gain displayed a positive association with MD, which was similar in both groups (OR = 1.17 per 6 kg; 95 % CI = 1.11-1.23). Women who had gained more than 24 kg displayed higher MD (OR = 2.05; 95 % CI = 1.53-2.73). MD was also evaluated using Wolfe's and Tabar's classifications, with similar results being obtained. Once BMI, fat distribution and other confounders were considered, our results showed a clear dose-response gradient between the number of kg gained during adulthood and the proportion of dense tissue in the breast.