Background Assessing the volume of mammographic density might more accurately reflect the amount of breast volume at risk of malignant transformation and provide a stronger indication of risk of breast cancer than methods based on qualitative scores or dense breast area. Methods We prospectively collected mammograms for women undergoing screening mammography. We determined the diagnosis of subsequent invasive or ductal carcinoma in situ (DCIS) for 275 cases, selected 825 controls matched for age, ethnicity, and mammography system, and assessed three measures of breast density: percent dense area, fibroglandular volume, and percent fibroglandular volume. Results After adjustment for familial breast cancer history, body mass index, history of breast biopsy, and age at first live birth, the odds ratios for breast cancer risk in the highest versus lowest measurement quintiles were 2.5 (95% CI: 1.5, 4.3) for percent dense area, 2.9 (95% CI: 1.7, 4.9) for fibroglandular volume, and 4.1 (95% CI: 2.3, 7.2) for percent fibroglandular volume. Net reclassification indexes for density measures plus risk factors versus risk factors alone were 9.6% (P=0.07) for percent dense area, 21.1% (P=0.0001) for fibroglandular volume, and 14.8% (P=0.004) for percent fibroglandular volume. Fibroglandular volume improved the categorical risk classification of 1 in 5 women for both women with and without breast cancer. Conclusion Volumetric measures of breast density are more accurate predictors of breast cancer risk than risk factors alone and than percent dense area. Impact Risk models including dense fibroglandular volume may more accurately predict breast cancer risk than current risk models.

Purpose Breast cancer risk assessment can inform the use of screening and prevention modalities. We investigated the performance of the Breast Cancer Surveillance Consortium (BCSC) risk model in combination with a polygenic risk score (PRS) comprised of 83 single nucleotide polymorphisms identified from genome wide association studies. Methods We conducted a nested case-control study of 486 cases and 495 matched controls within a screening cohort. The PRS was calculated using a Bayesian approach. The contributions of the PRS and variables in the BCSC model to breast cancer risk were tested using conditional logistic regression. Discriminatory accuracy of the models was compared using the area under the receiver operating characteristic curve (AUROC). Results Increasing quartiles of the PRS were positively associated with breast cancer risk, with OR 2.54 (95% CI 1.69-3.82) for breast cancer in the highest versus lowest quartile. In a multivariable model, the PRS, family history, and breast density remained strong risk factors. The AUROC of the PRS was 0.60 (95% CI 0.57-0.64), and an Asian-specific PRS had AUROC 0.64 (95% CI 0.53-0.74). A combined model including the BCSC risk factors and PRS had better discrimination than the BCSC model (AUROC 0.65 versus 0.62, p = 0.01). The BCSC-PRS model classified 18% of cases as high-risk (5-year risk ≥ 3%), compared with 7% using the BCSC model. Conclusion The PRS improved discrimination of the BCSC risk model and classified more cases as high-risk. Impact Further consideration of the PRS's role in decision-making around screening and prevention strategies is merited.

IntroductionUnderstanding whether mammographic density (MD) is associated with all breast tumor subtypes and whether the strength of association varies by age is important for utilizing MD in risk models.MethodsData were pooled from six studies including 3414 women with breast cancer and 7199 without who underwent screening mammography. Percent MD was assessed from digitized film-screen mammograms using a computer-assisted threshold technique. We used polytomous logistic regression to calculate breast cancer odds according to tumor type, histopathological characteristics, and receptor (estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor (HER2)) status by age (<55, 55–64, and ≥65 years).ResultsMD was positively associated with risk of invasive tumors across all ages, with a two-fold increased risk for high (>51%) versus average density (11-25%). Women ages <55 years with high MD had stronger increased risk of ductal carcinoma in situ (DCIS) compared to women ages 55–64 and ≥65 years (Page-interaction = 0.02). Among all ages, MD had a stronger association with large (>2.1 cm) versus small tumors and positive versus negative lymph node status (P’s < 0.01). For women ages <55 years, there was a stronger association of MD with ER-negative breast cancer than ER-positive tumors compared to women ages 55–64 and ≥65 years (Page-interaction = 0.04). MD was positively associated with both HER2-negative and HER2-positive tumors within each age group.ConclusionMD is strongly associated with all breast cancer subtypes, but particularly tumors of large size and positive lymph nodes across all ages, and ER-negative status among women ages <55 years, suggesting high MD may play an important role in tumor aggressiveness, especially in younger women.