Literature DB >> 25716949

Dense and nondense mammographic area and risk of breast cancer by age and tumor characteristics.

Kimberly A Bertrand1, Christopher G Scott2, Rulla M Tamimi1, Matthew R Jensen2, V Shane Pankratz2, Aaron D Norman3, Daniel W Visscher4, Fergus J Couch5, John Shepherd6, Yunn-Yi Chen7, Bo Fan6, Fang-Fang Wu2, Lin Ma8, Andrew H Beck9, Steven R Cummings10, Karla Kerlikowske11, Celine M Vachon12.   

Abstract

BACKGROUND: Mammographic density (MD) is a strong breast cancer risk factor. We previously reported associations of percent mammographic density (PMD) 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 PMD [dense area (DA) and nondense area (NDA)] with breast cancer subtypes.
METHODS: Data were pooled from six studies including 4,095 breast cancers and 8,558 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 histopathologic characteristics and receptor status were calculated using polytomous logistic regression.
RESULTS: DA was associated with increased breast cancer risk [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(+) versus ER(-) tumors (P(het) = 0.02), while NDA was more strongly associated with decreased risk of ER(-) versus ER(+) tumors (P(het) = 0.03).
CONCLUSIONS: DA and NDA have differential associations with ER(+) versus ER(-) tumors that vary by age. IMPACT: DA and NDA are important to consider when developing age- and subtype-specific risk models. ©2015 American Association for Cancer Research.

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Year:  2015        PMID: 25716949      PMCID: PMC4417380          DOI: 10.1158/1055-9965.EPI-14-1136

Source DB:  PubMed          Journal:  Cancer Epidemiol Biomarkers Prev        ISSN: 1055-9965            Impact factor:   4.254


  36 in total

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