Changes in breast cancer risk associated with benign breast disease from 1967 to 2013.

Background: Benign breast disease (BBD) increases breast cancer (BC) risk progressively for women diagnosed with nonproliferative change, proliferative disease without atypia (PDWA), and atypical hyperplasia (AH). Leveraging data from 18 704 women in the Mayo BBD Cohort (1967-2013), we evaluated temporal trends in BBD diagnoses and how they have influenced associated BC risk over 4 decades.

Methods: BC risk trends associated with BBD were evaluated using standardized incidence ratios (SIRs) and age-period-cohort modeling across 4 eras-premammogram (1967-1981), precore needle biopsy (CNB) (1982-1992), transition to CNB (1993-2001), and CNB era (2002-2013).

Investigating the added value of incorporatingmammographic density to an integrated breastcancer risk model with questionnaire-based riskfactors and polygenic risk score.

Introduction: Incorporation of mammographic density to breast cancer risk models could improve risk stratification to tailor screening and prevention strategies according to risk. Robust evaluation of the value of adding mammographic density to models with comprehensive information on questionnaire-based risk factors and polygenic risk score is needed to determine its effectiveness in improving risk stratification of such models.

Methods: We used the Individualized Coherent Absolute Risk Estimator (iCARE) tool for risk model building and validation to incorporate density to a previously validated literature-based model with questionnaire-based risk factors and a 313-variant polygenic risk score (PRS).

Polygenic score distribution differences across European ancestry populations: implications for breast cancer risk prediction.

Background: The 313-variant polygenic risk score (PRS) provides a promising tool for clinical breast cancer risk prediction. However, evaluation of the PRS across different European populations which could influence risk estimation has not been performed.

Methods: We explored the distribution of PRS across European populations using genotype data from 94,072 females without breast cancer diagnosis, of European-ancestry from 21 countries participating in the Breast Cancer Association Consortium (BCAC) and 223,316 females without breast cancer diagnosis from the UK Biobank.

Volumetric Breast Density Estimation From Three-Dimensional Reconstructed Digital Breast Tomosynthesis Images Using Deep Learning.

Purpose: Breast density is a widely established independent breast cancer risk factor. With the increasing utilization of digital breast tomosynthesis (DBT) in breast cancer screening, there is an opportunity to estimate volumetric breast density (VBD) routinely. However, current available methods extrapolate VBD from two-dimensional (2D) images acquired using DBT and/or depend on the existence of raw DBT data, which is rarely archived by clinical centers because of storage constraints.

Plasma prolactin and postmenopausal breast cancer risk: a pooled analysis of four prospective cohort studies.

Background: Prolactin, a hormone produced by the pituitary gland, regulates breast development and may contribute to breast cancer etiology. However, most epidemiologic studies of prolactin and breast cancer have been restricted to single, often small, study samples with limited exploration of effect modification.

Methods: The Biomarkers in Breast Cancer Risk Prediction consortium includes 8,279 postmenopausal women sampled from four prospective cohort studies, of whom 3,441 were diagnosed with invasive breast cancer after enrollment.