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Background: The breast dense tissue percentage on digital mammograms is one of the most commonly used markers for breast cancer risk estimation. Geometric features of dense tissue over the breast and the presence of texture structures contained in sliding windows that scan the mammograms may improve the predictive ability when combined with the breast dense tissue percentage., Methods: A case/control study nested within a screening program covering 1563 women with craniocaudal and mediolateral-oblique mammograms (755 controls and the contralateral breast mammograms at the closest screening visit before cancer diagnostic for 808 cases) aging 45 to 70 from Comunitat Valenciana (Spain) was used to extract geometric and texture features. The dense tissue segmentation was performed using DMScan and validated by two experienced radiologists. A model based on Random Forests was trained several times varying the set of variables. A training dataset of 1172 patients was evaluated with a 10-stratified-fold cross-validation scheme. The area under the Receiver Operating Characteristic curve (AUC) was the metric for the predictive ability. The results were assessed by only considering the output after applying the model to the test set, which was composed of the remaining 391 patients., Results: The AUC score obtained by the dense tissue percentage (0.55) was compared to a machine learning-based classifier results. The classifier, apart from the percentage of dense tissue of both views, firstly included global geometric features such as the distance of dense tissue to the pectoral muscle, dense tissue eccentricity or the dense tissue perimeter, obtaining an accuracy of 0.56. By the inclusion of a global feature based on local histograms of oriented gradients, the accuracy of the classifier was significantly improved (0.61). The number of well-classified patients was improved up to 236 when it was 208., Conclusion: Relative geometric features of dense tissue over the breast and histograms of standardized local texture features based on sliding windows scanning the whole breast improve risk prediction beyond the dense tissue percentage adjusted by geometrical variables. Other classifiers could improve the results obtained by the conventional Random Forests used in this study., (Copyright © 2019 Elsevier B.V. All rights reserved.)

We explored the relationship between sleep patterns and sleep disorders and mammographic density (MD), a marker of breast cancer risk. Participants in the DDM-Spain/var-DDM study, which included 2878 middle-aged Spanish women, were interviewed via telephone and asked questions on sleep characteristics. Two radiologists assessed MD in their left craneo-caudal mammogram, assisted by a validated semiautomatic-computer tool (DM-scan). We used log-transformed percentage MD as the dependent variable and fitted mixed linear regression models, including known confounding variables. Our results showed that neither sleeping patterns nor sleep disorders were associated with MD. However, women with frequent changes in their bedtime due to anxiety or depression had higher MD (e β :1.53;95%CI:1.04-2.26)., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Objective: To examine the association between two dietary patterns (Western and Mediterranean), previously linked to breast cancer risk, and mammographic density., Methods: This cross-sectional study included 3,584 women attending population-based breast cancer screening programs and recruited between October 7, 2007, and July 14, 2008 (participation rate 74.5%). Collected data included anthropometric measurements; demographic, obstetric, and gynecologic characteristics; family and personal health history; and diet in the preceding year. Mammographic density was blindly assessed by a single radiologist and classified into four categories: less than 10%, 10-25%, 25-50%, and greater than 50%. The association between adherence to either a Western or a Mediterranean dietary pattern and mammographic density was explored using multivariable ordinal logistic regression models with random center-specific intercepts. Models were adjusted for age, body mass index, parity, menopause, smoking, family history, hormonal treatment, and calorie and alcohol intake. Differences according to women's characteristics were tested including interaction terms., Results: Women with a higher adherence to the Western dietary pattern were more likely to have high mammographic density (n=242 [27%]) than women with low adherence (n=169 [19%]) with a fully adjusted odds ratio (ORQ4vsQ1) of 1.25 (95% confidence interval [CI] 1.03-1.52). This association was confined to overweight-obese women (adjusted ORQ4vsQ1 [95% CI] 1.41 [1.13-1.76]). No association between Mediterranean dietary pattern and mammographic density was observed., Conclusion: The Western dietary pattern was associated with increased mammographic density among overweight-obese women. Our results might inform specific dietary recommendations for women with high mammographic density.

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 Tabár'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.

High mammographic density (MD) is used as a phenotype risk marker for developing breast cancer. During pregnancy and lactation the breast attains full development, with a cellular-proliferation followed by a lobular-differentiation stage. This study investigates the influence of obstetric factors on MD among pre- and post-menopausal women. We enrolled 3,574 women aged 45-68 years who were participating in breast cancer screening programmes in seven screening centers. To measure MD, blind anonymous readings were taken by an experienced radiologist, using craniocaudal mammography and Boyd's semiquantitative scale. Demographic and reproductive data were directly surveyed by purpose-trained staff at the date of screening. The association between MD and obstetric variables was quantified by ordinal logistic regression, with screening centre introduced as a random effect term. We adjusted for age, number of children and body mass index, and stratified by menopausal status. Parity was inversely associated with density, the probability of having high MD decreased by 16% for each new birth (P value < 0.001). Among parous women, a positive association was detected with duration of lactation [>9 months: odds ratio (OR) = 1.33; 95% confidence interval (CI) = 1.02-1.72] and weight of first child (>3,500 g: OR = 1.32; 95% CI = 1.12-1.54). Age at first birth showed a different effect in pre- and post-menopausal women (P value for interaction = 0.030). No association was found among pre-menopausal women. However, in post-menopausal women the probability of having high MD increased in women who had their first child after the age of 30 (OR = 1.53; 95% CI = 1.17-2.00). A higher risk associated with birth of twins was also mainly observed in post-menopausal women (OR = 2.02; 95% CI = 1.18-3.46). Our study shows a greater prevalence of high MD in mothers of advanced age at first birth, those who had twins, those who have breastfed for longer periods, and mothers whose first child had an elevated birth weight. These results suggest the influence of hormones and growth factors over the proliferative activity of the mammary gland.