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Showing content from https://pubmed.ncbi.nlm.nih.gov/18316752/ below:

Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model

Affiliations

• ¹ Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, California 94143-1732, USA. jtice@medicine.ucsf.edu

• PMID: 18316752
• PMCID: PMC2674327
• DOI: 10.7326/0003-4819-148-5-200803040-00004

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Jeffrey A Tice et al. Ann Intern Med. 2008.

• ¹ Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, San Francisco, California 94143-1732, USA. jtice@medicine.ucsf.edu

• PMID: 18316752
• PMCID: PMC2674327
• DOI: 10.7326/0003-4819-148-5-200803040-00004

Item in Clipboard

Background: Current models for assessing breast cancer risk are complex and do not include breast density, a strong risk factor for breast cancer that is routinely reported with mammography.

Objective: To develop and validate an easy-to-use breast cancer risk prediction model that includes breast density.

Design: Empirical model based on Surveillance, Epidemiology, and End Results incidence, and relative hazards from a prospective cohort.

Setting: Screening mammography sites participating in the Breast Cancer Surveillance Consortium.

Patients: 1,095,484 women undergoing mammography who had no previous diagnosis of breast cancer.

Measurements: Self-reported age, race or ethnicity, family history of breast cancer, and history of breast biopsy. Community radiologists rated breast density by using 4 Breast Imaging Reporting and Data System categories.

Results: During 5.3 years of follow-up, invasive breast cancer was diagnosed in 14,766 women. The breast density model was well calibrated overall (expected-observed ratio, 1.03 [95% CI, 0.99 to 1.06]) and in racial and ethnic subgroups. It had modest discriminatory accuracy (concordance index, 0.66 [CI, 0.65 to 0.67]). Women with low-density mammograms had 5-year risks less than 1.67% unless they had a family history of breast cancer and were older than age 65 years.

Limitation: The model has only modest ability to discriminate between women who will develop breast cancer and those who will not.

Conclusion: A breast cancer prediction model that incorporates routinely reported measures of breast density can estimate 5-year risk for invasive breast cancer. Its accuracy needs to be further evaluated in independent populations before it can be recommended for clinical use.

PubMed Disclaimer

Potential Financial Conflicts of Interest: Consultancies: S.R. Cummings (Eli Lilly). Honoraria: S.R. Cummings (Eli Lilly). Grants received: J.A. Tice (Building Interdisciplinary Careers in Women's Health [career development award]), S.R. Cummings (Eli Lilly, Lilly Foundation). Grants pending: S.R. Cummings (Eli Lilly, Lilly Foundation).

BI-RADS = Breast Imaging…

BI-RADS = Breast Imaging Reporting and Data System; SEER = Surveillance, Epidemiology, and End Results.

• Summaries for patients. Using breast density to predict breast cancer risk.

  [No authors listed] [No authors listed] Ann Intern Med. 2008 Mar 4;148(5):I34. doi: 10.7326/0003-4819-148-5-200803040-00001. Ann Intern Med. 2008. PMID: 18316748 No abstract available.

• Mammographic breast density and the Gail model for breast cancer risk prediction in a screening population.

  Tice JA, Cummings SR, Ziv E, Kerlikowske K. Tice JA, et al. Breast Cancer Res Treat. 2005 Nov;94(2):115-22. doi: 10.1007/s10549-005-5152-4. Breast Cancer Res Treat. 2005. PMID: 16261410

• One versus Two Breast Density Measures to Predict 5- and 10-Year Breast Cancer Risk.

  Kerlikowske K, Gard CC, Sprague BL, Tice JA, Miglioretti DL; Breast Cancer Surveillance Consortium. Kerlikowske K, et al. Cancer Epidemiol Biomarkers Prev. 2015 Jun;24(6):889-97. doi: 10.1158/1055-9965.EPI-15-0035. Epub 2015 Mar 30. Cancer Epidemiol Biomarkers Prev. 2015. PMID: 25824444 Free PMC article.

• Prospective breast cancer risk prediction model for women undergoing screening mammography.

  Barlow WE, White E, Ballard-Barbash R, Vacek PM, Titus-Ernstoff L, Carney PA, Tice JA, Buist DS, Geller BM, Rosenberg R, Yankaskas BC, Kerlikowske K. Barlow WE, et al. J Natl Cancer Inst. 2006 Sep 6;98(17):1204-14. doi: 10.1093/jnci/djj331. J Natl Cancer Inst. 2006. PMID: 16954473

• Mammographic density, breast cancer risk and risk prediction.

  Vachon CM, van Gils CH, Sellers TA, Ghosh K, Pruthi S, Brandt KR, Pankratz VS. Vachon CM, et al. Breast Cancer Res. 2007;9(6):217. doi: 10.1186/bcr1829. Breast Cancer Res. 2007. PMID: 18190724 Free PMC article. Review.

• Mammographic density and breast cancer risk: current understanding and future prospects.

  Boyd NF, Martin LJ, Yaffe MJ, Minkin S. Boyd NF, et al. Breast Cancer Res. 2011;13(6):223. doi: 10.1186/bcr2942. Epub 2011 Nov 1. Breast Cancer Res. 2011. PMID: 22114898 Free PMC article. Review.

• Discriminatory Accuracy of the Gail Model for Breast Cancer Risk Assessment among Iranian Women.

  Rostami S, Rafei A, Damghanian M, Khakbazan Z, Maleki F, Zendehdel K. Rostami S, et al. Iran J Public Health. 2020 Nov;49(11):2205-2213. doi: 10.18502/ijph.v49i11.4739. Iran J Public Health. 2020. PMID: 33708742 Free PMC article.

• Model for individualized prediction of breast cancer risk after a benign breast biopsy.

  Pankratz VS, Degnim AC, Frank RD, Frost MH, Visscher DW, Vierkant RA, Hieken TJ, Ghosh K, Tarabishy Y, Vachon CM, Radisky DC, Hartmann LC. Pankratz VS, et al. J Clin Oncol. 2015 Mar 10;33(8):923-9. doi: 10.1200/JCO.2014.55.4865. Epub 2015 Jan 26. J Clin Oncol. 2015. PMID: 25624442 Free PMC article.

• Mammographic breast density patterns among a group of women in sub Saharan Africa.

  Galukande M, Kiguli-Malwadde E. Galukande M, et al. Afr Health Sci. 2012 Dec;12(4):422-5. doi: 10.4314/ahs.v12i4.4. Afr Health Sci. 2012. PMID: 23515353 Free PMC article.

• Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment.

  Gastounioti A, Conant EF, Kontos D. Gastounioti A, et al. Breast Cancer Res. 2016 Sep 20;18(1):91. doi: 10.1186/s13058-016-0755-8. Breast Cancer Res. 2016. PMID: 27645219 Free PMC article. Review.

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