Literature DB >> 22222356

Mammographic density, parity and age at first birth, and risk of breast cancer: an analysis of four case-control studies.

Christy G Woolcott1, Karin Koga, Shannon M Conroy, Celia Byrne, Chisato Nagata, Giske Ursin, Celine M Vachon, Martin J Yaffe, Ian Pagano, Gertraud Maskarinec.   

Abstract

Mammographic density is strongly and consistently associated with breast cancer risk. To determine if this association was modified by reproductive factors (parity and age at first birth), data were combined from four case-control studies conducted in the United States and Japan. To overcome the issue of variation in mammographic density assessment among the studies, a single observer re-read all the mammograms using one type of interactive thresholding software. Logistic regression was used to estimate odds ratios (OR) while adjusting for other known breast cancer risk factors. Included were 1,699 breast cancer cases and 2,422 controls, 74% of whom were postmenopausal. A positive association between mammographic density and breast cancer risk was evident in every group defined by parity and age at first birth (OR per doubling of percent mammographic density ranged between 1.20 and 1.39). Nonetheless, the association appeared to be stronger among nulliparous than parous women (OR per doubling of percent mammographic density = 1.39 vs. 1.24; P interaction = 0.054). However, when examined by study location, the effect modification by parity was apparent only in women from Hawaii and when examined by menopausal status, it was apparent in postmenopausal, but not premenopausal, women. Effect modification by parity was not significant in subgroups defined by body mass index or ethnicity. Adjusting for mammographic density did not attenuate the OR for the association between parity and breast cancer risk by more than 16.4%, suggesting that mammographic density explains only a small proportion of the reduction in breast cancer risk associated with parity. In conclusion, this study did not support the hypothesis that parity modifies the breast cancer risk attributed to mammographic density. Even though an effect modification was found in Hawaiian women, no such thing was found in women from the other three locations.

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Year:  2012        PMID: 22222356      PMCID: PMC3336030          DOI: 10.1007/s10549-011-1929-9

Source DB:  PubMed          Journal:  Breast Cancer Res Treat        ISSN: 0167-6806            Impact factor:   4.872


  46 in total

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Authors:  Pamela M Vacek; Berta M Geller
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2.  The quantitative analysis of mammographic densities.

Authors:  J W Byng; N F Boyd; E Fishell; R A Jong; M J Yaffe
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Authors:  Bryony S Wiseman; Zena Werb
Journal:  Science       Date:  2002-05-10       Impact factor: 47.728

Review 5.  Developmental, cellular, and molecular basis of human breast cancer.

Authors:  J Russo; Y F Hu; X Yang; I H Russo
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6.  Symmetry of projection in the quantitative analysis of mammographic images.

Authors:  J W Byng; N F Boyd; L Little; G Lockwood; E Fishell; R A Jong; M J Yaffe
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7.  Mammographic parenchymal patterns and risk of breast cancer at and after a prevalence screen in Singaporean women.

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8.  Mammographic density and breast cancer in three ethnic groups.

Authors:  Giske Ursin; Huiyan Ma; Anna H Wu; Leslie Bernstein; Martine Salane; Yuri R Parisky; Melvin Astrahan; Conchitina C Siozon; Malcolm C Pike
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2003-04       Impact factor: 4.254

9.  Breast cancer and breastfeeding: collaborative reanalysis of individual data from 47 epidemiological studies in 30 countries, including 50302 women with breast cancer and 96973 women without the disease.

Authors: 
Journal:  Lancet       Date:  2002-07-20       Impact factor: 79.321

10.  Interaction of dense breast patterns with other breast cancer risk factors in a case-control study.

Authors:  S W Duffy; R W Jakes; F C Ng; F Gao
Journal:  Br J Cancer       Date:  2004-07-19       Impact factor: 7.640

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  20 in total

1.  Methods for assessing and representing mammographic density: an analysis of 4 case-control studies.

Authors:  Christy G Woolcott; Shannon M Conroy; Chisato Nagata; Giske Ursin; Celine M Vachon; Martin J Yaffe; Ian S Pagano; Celia Byrne; Gertraud Maskarinec
Journal:  Am J Epidemiol       Date:  2013-10-11       Impact factor: 4.897

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Authors:  Eunjung Lee; Namphuong Doanvo; MiHee Lee; Zayar Soe; Alice W Lee; Cam Van Doan; Dennis Deapen; Giske Ursin; Darcy Spicer; Peggy Reynolds; Anna H Wu
Journal:  Cancer Causes Control       Date:  2020-01-08       Impact factor: 2.506

3.  Mammographic breast density and breast cancer risk: interactions of percent density, absolute dense, and non-dense areas with breast cancer risk factors.

Authors:  Lusine Yaghjyan; Graham A Colditz; Bernard Rosner; Rulla M Tamimi
Journal:  Breast Cancer Res Treat       Date:  2015-02-13       Impact factor: 4.872

4.  Dietary Fat Intake During Adolescence and Breast Density Among Young Women.

Authors:  Seungyoun Jung; Olga Goloubeva; Catherine Klifa; Erin S LeBlanc; Linda G Snetselaar; Linda Van Horn; Joanne F Dorgan
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2016-05-19       Impact factor: 4.254

5.  The utility of web mining for epidemiological research: studying the association between parity and cancer risk.

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Journal:  J Am Med Inform Assoc       Date:  2015-11-27       Impact factor: 4.497

6.  Racial Differences in Quantitative Measures of Area and Volumetric Breast Density.

Authors:  Anne Marie McCarthy; Brad M Keller; Lauren M Pantalone; Meng-Kang Hsieh; Marie Synnestvedt; Emily F Conant; Katrina Armstrong; Despina Kontos
Journal:  J Natl Cancer Inst       Date:  2016-04-29       Impact factor: 13.506

7.  Menstrual and reproductive characteristics and breast density in young women.

Authors:  Joanne F Dorgan; Catherine Klifa; Snehal Deshmukh; Brian L Egleston; John A Shepherd; Peter O Kwiterovich; Linda Van Horn; Linda G Snetselaar; Victor J Stevens; Alan M Robson; Norman L Lasser; Nola M Hylton
Journal:  Cancer Causes Control       Date:  2013-08-10       Impact factor: 2.506

8.  Inter-observer agreement according to three methods of evaluating mammographic density and parenchymal pattern in a case control study: impact on relative risk of breast cancer.

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9.  AutoDensity: an automated method to measure mammographic breast density that predicts breast cancer risk and screening outcomes.

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10.  Hormone metabolism pathway genes and mammographic density change after quitting estrogen and progestin combined hormone therapy in the California Teachers Study.

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Journal:  Breast Cancer Res       Date:  2014-12-11       Impact factor: 8.408

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