Journal of Preventive Medicine and Public Health

The Korean Society for Preventive Medicine (대한예방의학회)
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Breast Density and Risk of Breast Cancer in Asian Women: A Meta-analysis of Observational Studies

  • Bae, Jong-Myon (Department of Preventive Medicine, Jeju National University School of Medicine) ;
  • Kim, Eun Hee (Department of Preventive Medicine, Jeju National University School of Medicine)
  • Received : 2016.06.06
  • Accepted : 2016.10.21
  • Published : 2016.11.30
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Abstract

Objectives: The established theory that breast density is an independent predictor of breast cancer risk is based on studies targeting white women in the West. More Asian women than Western women have dense breasts, but the incidence of breast cancer is lower among Asian women. This meta-analysis investigated the association between breast density in mammography and breast cancer risk in Asian women. Methods: PubMed and Scopus were searched, and the final date of publication was set as December 31, 2015. The effect size in each article was calculated using the interval-collapse method. Summary effect sizes (sESs) and 95% confidence intervals (CIs) were calculated by conducting a meta-analysis applying a random effect model. To investigate the dose-response relationship, random effect dose-response meta-regression (RE-DRMR) was conducted. Results: Six analytical epidemiology studies in total were selected, including one cohort study and five case-control studies. A total of 17 datasets were constructed by type of breast density index and menopausal status. In analyzing the subgroups of premenopausal vs. postmenopausal women, the percent density (PD) index was confirmed to be associated with a significantly elevated risk for breast cancer (sES, 2.21; 95% CI, 1.52 to 3.21; $I^2=50.0%$). The RE-DRMR results showed that the risk of breast cancer increased 1.73 times for each 25% increase in PD in postmenopausal women (95% CI, 1.20 to 2.47). Conclusions: In Asian women, breast cancer risk increased with breast density measured using the PD index, regardless of menopausal status. We propose the further development of a breast cancer risk prediction model based on the application of PD in Asian women.

Keywords

References

  1. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin 2015;65(2):87-108. https://doi.org/10.3322/caac.21262
  2. Pettersson A, Hankinson SE, Willett WC, Lagiou P, Trichopoulos D, Tamimi RM. Nondense mammographic area and risk of breast cancer. Breast Cancer Res 2011;13(5):R100. https://doi.org/10.1186/bcr3041
  3. Martin LJ, Boyd NF. Mammographic density. Potential mechanisms of breast cancer risk associated with mammographic density: hypotheses based on epidemiological evidence. Breast Cancer Res 2008;10(1):201. https://doi.org/10.1186/bcr1831
  4. Boyd NF, Martin LJ, Yaffe MJ, Minkin S. Mammographic density and breast cancer risk: current understanding and future prospects. Breast Cancer Res 2011;13(6):223. https://doi.org/10.1186/bcr2942
  5. Saftlas AF, Szklo M. Mammographic parenchymal patterns and breast cancer risk. Epidemiol Rev 1987;9:146-174. https://doi.org/10.1093/oxfordjournals.epirev.a036300
  6. Warner E, Lockwood G, Tritchler D, Boyd NF. The risk of breast cancer associated with mammographic parenchymal patterns: a meta-analysis of the published literature to examine the effect of method of classification. Cancer Detect Prev 1992;16(1):67-72.
  7. Oza AM, Boyd NF. Mammographic parenchymal patterns: a marker of breast cancer risk. Epidemiol Rev 1993;15(1):196-208. https://doi.org/10.1093/oxfordjournals.epirev.a036105
  8. Boyd NF, Lockwood GA, Byng JW, Tritchler DL, Yaffe MJ. Mammographic densities and breast cancer risk. Cancer Epidemiol Biomarkers Prev 1998;7(12):1133-1144.
  9. McCormack VA, dos Santos Silva I. Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev 2006;15(6):1159-1169. https://doi.org/10.1158/1055-9965.EPI-06-0034
  10. Razzaghi H, Troester MA, Gierach GL, Olshan AF, Yankaskas BC, Millikan RC. Mammographic density and breast cancer risk in white and African American women. Breast Cancer Res Treat 2012;135(2):571-580. https://doi.org/10.1007/s10549-012-2185-3
  11. Habel LA, Capra AM, Oestreicher N, Greendale GA, Cauley JA, Bromberger J, et al. Mammographic density in a multiethnic cohort. Menopause 2007;14(5):891-899. https://doi.org/10.1097/gme.0b013e318032569c
  12. del Carmen MG, Hughes KS, Halpern E, Rafferty E, Kopans D, Parisky YR, et al. Racial differences in mammographic breast density. Cancer 2003;98(3):590-596. https://doi.org/10.1002/cncr.11517
  13. El-Bastawissi AY, White E, Mandelson MT, Taplin S. Variation in mammographic breast density by race. Ann Epidemiol 2001; 11(4):257-263. https://doi.org/10.1016/S1047-2797(00)00225-8
  14. Tan SM, Evans AJ, Lam TP, Cheung KL. How relevant is breast cancer screening in the Asia/Pacific region? Breast 2007;16(2): 113-119. https://doi.org/10.1016/j.breast.2006.08.005
  15. Maskarinec G, Meng L, Ursin G. Ethnic differences in mammographic densities. Int J Epidemiol 2001;30(5):959-965. https://doi.org/10.1093/ije/30.5.959
  16. Chen Z, Wu AH, Gauderman WJ, Bernstein L, Ma H, Pike MC, et al. Does mammographic density reflect ethnic differences in breast cancer incidence rates? Am J Epidemiol 2004;159(2): 140-147. https://doi.org/10.1093/aje/kwh028
  17. Kawahara M, Sato S, Ida Y, Watanabe M, Fujishima M, Ishii H, et al. Factors influencing breast density in Japanese women aged 40-49 in breast cancer screening mammography. Acta Med Okayama 2013;67(4):213-217.
  18. Bae JM. Two hypotheses of dense breasts and viral infection for explaining incidence of breast cancer by age group in Korean women. Epidemiol Health 2014;36:e2014020.
  19. Shin HR, Joubert C, Boniol M, Hery C, Ahn SH, Won YJ, et al. Recent trends and patterns in breast cancer incidence among Eastern and Southeastern Asian women. Cancer Causes Control 2010;21(11):1777-1785. https://doi.org/10.1007/s10552-010-9604-8
  20. Kim BK, Choi YH, Nguyen TL, Nam SJ, Lee JE, Hopper JL, et al. Mammographic density and risk of breast cancer in Korean women. Eur J Cancer Prev 2015;24(5):422-429. https://doi.org/10.1097/CEJ.0000000000000099
  21. Nagata C, Matsubara T, Fujita H, Nagao Y, Shibuya C, Kashiki Y, et al. Mammographic density and the risk of breast cancer in Japanese women. Br J Cancer 2005;92(12):.2102-2106. https://doi.org/10.1038/sj.bjc.6602643
  22. Kotsuma Y, Tamaki Y, Nishimura T, Tsubai M, Ueda S, Shimazu K, et al. Quantitative assessment of mammographic density and breast cancer risk for Japanese women. Breast 2008;17(1):27-35. https://doi.org/10.1016/j.breast.2007.06.002
  23. Wong CS, Lim GH, Gao F, Jakes RW, Offman J, Chia KS, et al. Mammographic density and its interaction with other breast cancer risk factors in an Asian population. Br J Cancer 2011; 104(5):871-874. https://doi.org/10.1038/sj.bjc.6606085
  24. Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gotzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ 2009; 339:b2700. https://doi.org/10.1136/bmj.b2700
  25. Bae JM, Kim EH. Citation discovery tools for conducting adaptive meta-analyses to update systematic reviews. J Prev Med Public Health 2016;49(2):129-133. https://doi.org/10.3961/jpmph.15.074
  26. Bae JM. Comparison of methods of extracting information for meta-analysis of observational studies in nutritional epidemiology. Epidemiol Health 2016;38:e2016003. https://doi.org/10.4178/epih.e2016003
  27. Orsini N, Bellocco R, Greenland S. Generalized least squares for trend estimation of summarized dose-response data. Stata J 2006;6(1):40-57.
  28. Nagao Y, Kawaguchi Y, Sugiyama Y, Saji S, Kashiki Y. Relationship between mammographic density and the risk of breast cancer in Japanese women: a case-control study. Breast Cancer 2003;10(3):228-233. https://doi.org/10.1007/BF02966722
  29. Park IH, Ko K, Joo J, Park B, Jung SY, Lee S, et al. High volumetric breast density predicts risk for breast cancer in postmenopausal, but not premenopausal, Korean women. Ann Surg Oncol 2014;21(13):4124-4132. https://doi.org/10.1245/s10434-014-3832-1
  30. Lee CP, Choi H, Soo KC, Tan MH, Chay WY, Chia KS, et al. Mammographic breast density and common genetic variants in breast cancer risk prediction. PLoS One 2015;10(9):e0136650. https://doi.org/10.1371/journal.pone.0136650
  31. Woolcott CG, Koga K, Conroy SM, Byrne C, Nagata C, Ursin G, et al. Mammographic density, parity and age at first birth, and risk of breast cancer: an analysis of four case-control studies. Breast Cancer Res Treat 2012;132(3):1163-1171. https://doi.org/10.1007/s10549-011-1929-9
  32. Conroy SM, Woolcott CG, Koga KR, Byrne C, Nagata C, Ursin G, et al. Mammographic density and risk of breast cancer by adiposity: an analysis of four case-control studies. Int J Cancer 2012;130(8):1915-1924. https://doi.org/10.1002/ijc.26205
  33. Conroy SM, Koga K, Woolcott CG, Dahl T, Byrne C, Nagata C, et al. Higher alcohol intake may modify the association between mammographic density and breast cancer: an analysis of three case-control studies. Cancer Epidemiol 2012;36(5):458-460. https://doi.org/10.1016/j.canep.2012.06.007
  34. Maskarinec G, Nakamura KL, Woolcott CG, Conroy SM, Byrne C, Nagata C, et al. Mammographic density and breast cancer risk by family history in women of white and Asian ancestry. Cancer Causes Control 2015;26(4):621-626. https://doi.org/10.1007/s10552-015-0551-2
  35. Jakes RW, Duffy SW, Ng FC, Gao F, Ng EH. Mammographic parenchymal patterns and risk of breast cancer at and after a prevalence screen in Singaporean women. Int J Epidemiol 2000;29(1):11-19. https://doi.org/10.1093/ije/29.1.11
  36. Nguyen TL, Aung YK, Evans CF, Yoon-Ho C, Jenkins MA5, Sung J, et al. Mammographic density defined by higher than conventional brightness threshold better predicts breast cancer risk for full-field digital mammograms. Breast Cancer Res 2015;17:142. https://doi.org/10.1186/s13058-015-0654-4
  37. Lai CW, Law HK. Mammographic breast density in chinese women: spatial distribution and autocorrelation patterns. PLoS One 2015;10(9):e0136881. https://doi.org/10.1371/journal.pone.0136881
  38. Jeon JH, Kang JH, Kim Y, Lee HY, Choi KS, Jun JK, et al. Reproductive and hormonal factors associated with fatty or dense breast patterns among Korean women. Cancer Res Treat 2011; 43(1):42-48. https://doi.org/10.4143/crt.2011.43.1.42
  39. Dite GS, Gurrin LC, Byrnes GB, Stone J, Gunasekara A, McCredie MR, et al. Predictors of mammographic density: insights gained from a novel regression analysis of a twin study. Cancer Epidemiol Biomarkers Prev 2008;17(12):3474-3481. https://doi.org/10.1158/1055-9965.EPI-07-2636
  40. Boyd NF, Rommens JM, Vogt K, Lee V, Hopper JL, Yaffe MJ, et al. Mammographic breast density as an intermediate phenotype for breast cancer. Lancet Oncol 2005;6(10):798-808. https://doi.org/10.1016/S1470-2045(05)70390-9
  41. Machida Y, Tozaki M, Shimauchi A, Yoshida T. Breast density: the trend in breast cancer screening. Breast Cancer 2015; 22(3):253-261. https://doi.org/10.1007/s12282-015-0602-2
  42. Tan YY, Wee SB, Tan MP, Chong BK. Positive predictive value of BI-RADS categorization in an Asian population. Asian J Surg 2004;27(3):186-191. https://doi.org/10.1016/S1015-9584(09)60030-0
  43. Colin C, Prince V, Valette PJ. Can mammographic assessments lead to consider density as a risk factor for breast cancer? Eur J Radiol 2013;82(3):404-411. https://doi.org/10.1016/j.ejrad.2010.01.001
  44. Kopans DB. Basic physics and doubts about relationship between mammographically determined tissue density and breast cancer risk. Radiology 2008;246(2):348-353. https://doi.org/10.1148/radiol.2461070309
  45. Ursin G, Ma H, Wu AH, Bernstein L, Salane M, Parisky YR, et al. Mammographic density and breast cancer in three ethnic groups. Cancer Epidemiol Biomarkers Prev 2003;12(4):332-338.
  46. Maskarinec G, Pagano I, Chen Z, Nagata C, Gram IT. Ethnic and geographic differences in mammographic density and their association with breast cancer incidence. Breast Cancer Res Treat 2007;104(1):47-56. https://doi.org/10.1007/s10549-006-9387-5
  47. Bae JM, Shin SY, Kim EH, Kim YN, Nam CM. Distribution of dense breasts using screening mammography in Korean women: a retrospective observational study. Epidemiol Health 2014;36:e2014027.
  48. Kim EH, Bae JM. Potential availability of dense mammogrphy for prevention of breast cancer in Korean women. Korean J Fam Pract 2014;4(3):181-185 (Korean).

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