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We evaluated whether a 76-locus polygenic risk score (PRS) and Breast Imaging Reporting and Data System (BI-RADS) breast density were independent risk factors within three studies (1643 case patients, 2397 control patients) using logistic regression models. We incorporated the PRS odds ratio (OR) into the Breast Cancer Surveillance Consortium (BCSC) risk-prediction model while accounting for its attributable risk and compared five-year absolute risk predictions between models using area under the curve (AUC) statistics. All statistical tests were two-sided. BI-RADS density and PRS were independent risk factors across all three studies (P interaction = .23). Relative to those with scattered fibroglandular densities and average PRS (2(nd) quartile), women with extreme density and highest quartile PRS had 2.7-fold (95% confidence interval [CI] = 1.74 to 4.12) increased risk, while those with low density and PRS had reduced risk (OR = 0.30, 95% CI = 0.18 to 0.51). PRS added independent information (P < .001) to the BCSC model and improved discriminatory accuracy from AUC = 0.66 to AUC = 0.69. Although the BCSC-PRS model was well calibrated in case-control data, independent cohort data are needed to test calibration in the general population., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Background: Breast volume is a relevant measure for the prevention and prediction of diseases and for aesthetic surgery. This study evaluated a new technique to determine breast volume and compared measures using a three-dimensional (3D) body surface scanner and magnetic resonance imaging (MRI) scans, with the latter used as the standard method., Methods: Both MRI scans and body surface 3D scans were obtained from 22 women. For each method, breast volumes were assessed. The MRI calculations of the breast volumes were performed by a specially trained radiologist using analysis software. A textured 3D image was generated by a calibrated digital texture camera after breast surface data acquisition. The volume assessment of the 3D photography was calculated using a software package after manual outlining of the breast and automated projection of a dorsal limit. Linear regression was used to predict the MRI volume assessment with the 3D image volume assessment., Results: The mean breast volume according to MRI volumetry was 442.8 ml on the left side and 471.8 ml on the right side. The mean breast volume using a 3D body surface volume assessment method was 273.8 ml (observer A) and 226.2 ml (observer B) on the left side and 284.4 ml (observer A) and 234.9 ml (observer B) on the right side. The use of linear regression models showed R (2) values of 0.59-0.77. The mean time for MRI recording and volume assessment was 68.0 ± 14.1 min for both sides and 11.6 ± 1.5 min for 3D recording and volume assessment., Conclusions: The 3D surface-based volume measurements are feasible in terms of time and can predict the MRI breast volume with sufficient accuracy. This might facilitate the broad use of such an assessment technique in a large-scale epidemiologic study using breast volume as a study aim. Additionally, further development of volume assessments could help to implement this technique in breast surgery procedures.