IntroductionMammographic density is a strong breast cancer risk factor and a major determinant of screening sensitivity. However, there is currently no validated estimation method for full-field digital mammography (FFDM).MethodsThe performance of three area-based approaches (BI-RADS, the semi-automated Cumulus, and the fully-automated ImageJ-based approach) and three fully-automated volumetric methods (Volpara, Quantra and single energy x-ray absorptiometry (SXA)) were assessed in 3168 FFDM images from 414 cases and 685 controls. Linear regression models were used to assess associations between breast cancer risk factors and density among controls, and logistic regression models to assess density-breast cancer risk associations, adjusting for age, body mass index (BMI) and reproductive variables.ResultsQuantra and the ImageJ-based approach failed to produce readings for 4% and 11% of the participants. All six density assessment methods showed that percent density (PD) was inversely associated with age, BMI, being parous and postmenopausal at mammography. PD was positively associated with breast cancer for all methods, but with the increase in risk per standard deviation increment in PD being highest for Volpara (1.83; 95% CI: 1.51 to 2.21) and Cumulus (1.58; 1.33 to 1.88) and lower for the ImageJ-based method (1.45; 1.21 to 1.74), Quantra (1.40; 1.19 to 1.66) and SXA (1.37; 1.16 to 1.63). Women in the top PD quintile (or BI-RADS 4) had 8.26 (4.28 to 15.96), 3.94 (2.26 to 6.86), 3.38 (2.00 to 5.72), 2.99 (1.76 to 5.09), 2.55 (1.46 to 4.43) and 2.96 (0.50 to 17.5) times the risk of those in the bottom one (or BI-RADS 1), respectively, for Volpara, Quantra, Cumulus, SXA, ImageJ-based method, and BI-RADS (P for trend <0.0001 for all). The ImageJ-based method had a slightly higher ability to discriminate between cases and controls (area under the curve (AUC) for PD = 0.68, P = 0.05), and Quantra slightly lower (AUC = 0.63; P = 0.06), than Cumulus (AUC = 0.65).ConclusionsFully-automated methods are valid alternatives to the labour-intensive "gold standard" Cumulus for quantifying density in FFDM. The choice of a particular method will depend on the aims and setting but the same approach will be required for longitudinal density assessments.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-014-0439-1) contains supplementary material, which is available to authorized users.

Objectives:We conducted a population-based survey to examine gender differences in occupational exposure patterns and to investigate whether any observed differences are due to: a) gender differences in occupational distribution; and/or b) gender differences in tasks within occupations. Methods:Men and women aged 20-64 years were randomly selected from the Electoral Roll and invited to take part in a telephone interview, which collected information on selfreported occupational exposure to specific dusts and chemicals, physical exposures, and organisational factors. We used logistic regression to calculate prevalence odds ratios (OR) and 95% confidence intervals (CI) comparing the exposure prevalence of males (n=1,431) and females (n=1,572), adjusting for age. To investigate whether men and women in the same occupation were equally exposed, we also matched males to females on current occupation (5-digit code) (n=1,208) and conducted conditional logistic regression adjusting for age. Results:Overall, male workers were two to four times more likely to report exposure to dust and chemical substances, loud noise, irregular hours, night shifts, and vibrating tools. Women were 30% more likely to report repetitive tasks and working at high speed and more likely to report exposure to disinfectants, hair dyes, and textile dust. When men were compared with women with the same job title, gender differences were attenuated. However, males remained significantly more likely to report exposure to welding fumes, herbicides, wood dust, solvents, tools that vibrate, irregular hours, and night shift work. Women remained more likely to report repetitive tasks and working at high speed, and in addition were more likely to report awkward or tiring positions compared to men with the same job title. Conclusion:This population-based study showed substantial differences in occupational exposure patterns between men and women, due to both gender differences in occupational distribution as well as the gender segregation of tasks within the same job.3

In a systematic review and meta-analysis, Isabel dos Santos Silva and colleagues estimate the prevalence of receptor-defined subtypes of breast cancer in North Africa and sub-Saharan Africa. Please see later in the article for the Editors' Summary