One vs. Two Breast Density Measures to Predict 5- and 10- Year Breast Cancer Risk
Menée aux Etats-Unis à partir de mammographies de 722 654 femmes âgées de 34 à 74 ans, cette étude analyse l'efficacité d'un modèle de prédiction du risque basé sur deux mesures de la densité mammaire pour prédire le risque de cancer du sein à 5 et 10 ans
Background: One measure of Breast Imaging Reporting and Data System (BI-RADS) breast density improves 5-year breast cancer risk prediction, but the value of sequential measures is unknown. We determined if two BI-RADS density measures improves the predictive accuracy of the Breast Cancer Surveillance Consortium 5-year risk model compared to one measure.
Methods: We included 722,654 women aged 35-74 years with two mammograms with BI-RADS density measures on average 1.8 years apart; 13,715 developed invasive breast cancer. We used Cox regression to estimate the relative hazards of breast cancer for age, race/ethnicity, family history of breast cancer, history of breast biopsy, and one or two density measures. We developed a risk prediction model by combining these estimates with 2000-2010 Surveillance, Epidemiology, and End Results incidence and 2010 vital statistics for competing risk of death.
Results: The two-measure density model had marginally greater discriminatory accuracy than the one-measure model (AUC=0.640 vs. 0.635). Of 18.6% of women (134,404/722,654) who decreased density categories, 15.4% (20,741/134,404) of women whose density decreased from heterogeneously or extremely dense to a lower density category with one other risk factor had a clinically meaningful increase in 5-year risk from <1.67% with the one-density model to ≥1.67% with the two-density model.
Conclusion: The two-density model has similar overall discrimination to the one-density model for predicting 5-year breast cancer risk and improves risk classification for women with risk factors and a decrease in density. Impact: A two-density model should be considered for women whose density decreases when calculating breast cancer risk.
Cancer Epidemiology Biomarkers & Prevention , résumé, 2015