期刊
CANCER EPIDEMIOLOGY BIOMARKERS & PREVENTION
卷 26, 期 10, 页码 1487-1492出版社
AMER ASSOC CANCER RESEARCH
DOI: 10.1158/1055-9965.EPI-16-0881
关键词
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资金
- NIH [CA163177]
- Department of Defense [BC100474, W81XWH-11-1-0545, A-17074]
Background: Mammographic density (MD) is associated with increased breast cancer risk, yet limited data exist on an association between MD and breast cancer molecular subtypes. Methods: Women ages 18 years and older with breast cancer and available mammograms between 2003 and 2012 were enrolled in a larger study on MD. MD was classified by the Breast Imaging Reporting and Data System (BI-RADS) classification and by volumetric breast percent density (Volpara Solutions). Subtype was assigned by hormone receptor status, tumor grade, and mitotic score (MS). Subtypes included: Luminal-A (ER/PR- and grade = 1; ER/PR_ and grade = 2 and MS = 1; ER+/PR- and grade = 1; n = 233); Luminal-B (ER+ and grade = 3orMS = 3; ER+/PR- and grade = 2; ER/PR_ and grade = 2 and MS = 2; n = 79); Her-2-neu (H2P; n = 59); triple-negative (ER/PR-, Her-2(-); n = 86). Precancer factors including age, race, body mass index (kg/m(2)), family history of breast cancer, and history of lobular carcinoma in situ were analyzed. Results: A total of 604 patients had invasive cancer; 457 had sufficient information for analysis. Women with H2P tumors were younger (P = 0.011) and had the highest volumetric percent density (P = 0.002) among subgroups. Multinomial logistic regression (LA = reference) demonstrated that although quantitative MD does not significantly differentiate between all subtypes (P = 0.123), the association between MD and H2P tumors is significant (OR = 1.06; confidence interval, 1.01-1.12). This association was not seen using BI-RADS classification in bivariable analysis but was statistically significant (P = 0.047) when controlling for other precancer factors. Conclusions: Increased MD is more strongly associated with H2P tumors when compared with LA. Impact: Delineating risk factors specific to breast cancer subtype may promote development of individualized risk prediction models and screening strategies. (C) 2017 AACR.
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