Dense and Non-dense Mammographic Area and Risk of Breast Cancer by Age and Tumor Characteristics.

Citation:

Bertrand KA, Scott CG, Tamimi RM, Jensen MR, Pankratz SV, Norman AD, Visscher DW, Couch FJ, Shepherd J, Chen Y-Y, Fan B, Wu F-F, Ma L, Beck AH, Cummings SR, Kerlikowske K, Vachon CM. Dense and Non-dense Mammographic Area and Risk of Breast Cancer by Age and Tumor Characteristics. Cancer Epidemiol Biomarkers Prev 2015;

Date Published:

2015 Feb 25

Abstract:

Background:Mammographic density (MD) is a strong breast cancer risk factor. We previously reported associations of percent MD with larger and node-positive tumors across all ages, and estrogen receptor (ER)-negative status among women ages <55 years. To provide insight into these associations, we examined the components of percent MD (dense area (DA) and non-dense area (NDA) with breast cancer subtypes. Methods:Data were pooled from six studies including 4095 breast cancers and 8558 controls. DA and NDA were assessed from digitized film-screen mammograms and standardized across studies. Breast cancer odds by density phenotypes and age according to histopathological characteristics and receptor status were calculated using polytomous logistic regression. Results:DA was associated with increased breast cancer risk [odds ratios (OR) for quartiles: 0.65, 1.00(Ref), 1.22, 1.55; p-trend <0.001] and NDA was associated with decreased risk [ORs for quartiles: 1.39, 1.00(Ref), 0.88, 0.72; p-trend <0.001] across all ages and invasive tumor characteristics. There were significant trends in the magnitude of associations of both DA and NDA with breast cancer by increasing tumor size (p-trend<0.001) but no differences by nodal status. Among women <55 years, DA was more strongly associated with increased risk of ER+ vs. ER- tumors [p-heterogeneity (het) = 0.02] while NDA was more strongly associated with decreased risk of ER- vs. ER+ tumors [p-het = 0.03]. Conclusions:DA and NDA have differential associations with ER+ vs. ER- tumors that vary by age. Impact:DA and NDA are important to consider when developing age- and subtype-specific risk models.