Dose-dependent effect of mammographic breast density on the risk of contralateral breast cancer
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Increased mammographic breast density is a significant risk factor for breast cancer. It is not clear if it is also a risk factor for the development of contralateral breast cancer.
The data were obtained from Breast Cancer Surveillance Consortium and included women diagnosed with invasive breast cancer or ductal carcinoma in situ between ages 18 and 88 and years 1995 and 2009. Each case of contralateral breast cancer was matched with three controls based on year of first breast cancer diagnosis, race, and length of follow-up. A total of 847 cases and 2541 controls were included. The risk factors included in the study were mammographic breast density, age of first breast cancer diagnosis, family history of breast cancer, anti-estrogen treatment, hormone replacement therapy, menopausal status, and estrogen receptor status, all from the time of first breast cancer diagnosis. Both univariate analysis and multivariate conditional logistic regression analysis were performed.
In the final multivariate model, breast density, family history of breast cancer, and anti-estrogen treatment remained significant with p values less than 0.01. Increasing breast density had a dose-dependent effect on the risk of contralateral breast cancer. Relative to ‘almost entirely fat’ category of breast density, the adjusted odds ratios (and p values) in the multivariate analysis for ‘scattered density,’ ‘heterogeneously dense,’ and ‘extremely dense’ categories were 1.65 (0.036), 2.10 (0.002), and 2.32 (0.001), respectively.
Breast density is an independent and significant risk factor for development of contralateral breast cancer. This risk factor should contribute to clinical decision making.
KeywordsContralateral breast cancer Breast density Breast Cancer Surveillance Consortium Contralateral prophylactic mastectomy
This work was supported in part by the National Cancer Institute-funded Breast Cancer Surveillance Consortium (HHSN261201100031C). The collection of cancer and vital status data used in this study was supported in part by several state public health departments and cancer registries throughout the US. For a full description of these sources, please see: http://breastscreening.cancer.gov/work/acknowledgement.html. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health. We thank the participating women, mammography facilities, and radiologists for the data they have provided for this study. You can learn more about the BCSC at: http://www.bcsc-research.org/. We thank Linn Abraham for providing BCSC data-related support. We are also thankful to an anonymous reviewer for providing constructive comments, which led to an improved version of the paper.
This work was funded by the National Cancer Institute at the National Institutes of Health (Grant Number R21 CA186086). This work was also supported in part by the National Cancer Institute-funded Breast Cancer Surveillance Consortium (HHSN261201100031C).
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Conflict of interest
The authors declare that they have no conflict of interest.
- 8.Uyeno L, Behrendt, Vito C (2012) Contralateral breast cancer: Impact on survival after unilateral breast cancer is stage-dependent. ASCO Breast Cancer Symposium Abstract vol 69Google Scholar
- 29.Breast Cancer Surveillance Consortium (http://breastscreening.cancer.gov/) (2016). National Cancer Institute, Applied Research Program
- 30.Breslow NE, Day NE (1980) Statistical Methods in Cancer Research. International Agency for Research on Cancer, LyonGoogle Scholar
- 32.Hosmer DW, Lemeshow S (2000) Applied Logistic Regression, 2nd ed. Wiley, hobokenGoogle Scholar
- 33.Development Core Team R (2016) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
- 34.Therneau TM (2015) A package for survival analysis in S. version 2.41-3. https://CRAN.R-project.org/package=survival Accessed Nov 22 2017