Literature DB >> 25217577

Prevalence of mammographically dense breasts in the United States.

Brian L Sprague1, Ronald E Gangnon2, Veronica Burt2, Amy Trentham-Dietz2, John M Hampton2, Robert D Wellman2, Karla Kerlikowske2, Diana L Miglioretti2.   

Abstract

BACKGROUND: National legislation is under consideration that would require women with mammographically dense breasts to be informed of their breast density and encouraged to discuss supplemental breast cancer screening with their health care providers. The number of US women potentially affected by this legislation is unknown.
METHODS: We determined the mammographic breast density distribution by age and body mass index (BMI) using data from 1518 599 mammograms conducted from 2007 through 2010 at mammography facilities in the Breast Cancer Surveillance Consortium (BCSC). We applied these breast density distributions to age- and BMI-specific counts of the US female population derived from the 2010 US Census and the National Health and Nutrition Examination Survey (NHANES) to estimate the number of US women with dense breasts.
RESULTS: Overall, 43.3% (95% confidence interval [CI] = 43.1% to 43.4%) of women 40 to 74 years of age had heterogeneously or extremely dense breasts, and this proportion was inversely associated with age and BMI. Based on the age and BMI distribution of US women, we estimated that 27.6 million women (95% CI = 27.5 to 27.7 million) aged 40 to 74 years in the United States have heterogeneously or extremely dense breasts. Women aged 40 to 49 years (N = 12.3 million) accounted for 44.3% of this group.
CONCLUSION: The prevalence of dense breasts among US women of common breast cancer screening ages exceeds 25 million. Policymakers and healthcare providers should consider this large prevalence when debating breast density notification legislation and designing strategies to ensure that women who are notified have opportunities to evaluate breast cancer risk and discuss and pursue supplemental screening options if deemed appropriate.
© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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Year:  2014        PMID: 25217577      PMCID: PMC4200066          DOI: 10.1093/jnci/dju255

Source DB:  PubMed          Journal:  J Natl Cancer Inst        ISSN: 0027-8874            Impact factor:   13.506


  22 in total

1.  Association of mammographically defined percent breast density with epidemiologic risk factors for breast cancer (United States).

Authors:  C M Vachon; C C Kuni; K Anderson; V E Anderson; T A Sellers
Journal:  Cancer Causes Control       Date:  2000-08       Impact factor: 2.506

2.  Mammographic density and the risk and detection of breast cancer.

Authors:  Norman F Boyd; Helen Guo; Lisa J Martin; Limei Sun; Jennifer Stone; Eve Fishell; Roberta A Jong; Greg Hislop; Anna Chiarelli; Salomon Minkin; Martin J Yaffe
Journal:  N Engl J Med       Date:  2007-01-18       Impact factor: 91.245

Review 3.  A critical look at methods for handling missing covariates in epidemiologic regression analyses.

Authors:  S Greenland; W D Finkle
Journal:  Am J Epidemiol       Date:  1995-12-15       Impact factor: 4.897

4.  Height and weight, mammographic features of breast tissue, and breast cancer risk.

Authors:  J Brisson; A S Morrison; D B Kopans; N L Sadowsky; L Kalisher; J A Twaddle; J E Meyer; C I Henschke; P Cole
Journal:  Am J Epidemiol       Date:  1984-03       Impact factor: 4.897

5.  Diagnostic performance of digital versus film mammography for breast-cancer screening.

Authors:  Etta D Pisano; Constantine Gatsonis; Edward Hendrick; Martin Yaffe; Janet K Baum; Suddhasatta Acharyya; Emily F Conant; Laurie L Fajardo; Lawrence Bassett; Carl D'Orsi; Roberta Jong; Murray Rebner
Journal:  N Engl J Med       Date:  2005-09-16       Impact factor: 91.245

6.  Performance benchmarks for diagnostic mammography.

Authors:  Edward A Sickles; Diana L Miglioretti; Rachel Ballard-Barbash; Berta M Geller; Jessica W T Leung; Robert D Rosenberg; Rebecca Smith-Bindman; Bonnie C Yankaskas
Journal:  Radiology       Date:  2005-06       Impact factor: 11.105

7.  Effect of age, breast density, and family history on the sensitivity of first screening mammography.

Authors:  K Kerlikowske; D Grady; J Barclay; E A Sickles; V Ernster
Journal:  JAMA       Date:  1996-07-03       Impact factor: 56.272

8.  Factors contributing to mammography failure in women aged 40-49 years.

Authors:  Diana S M Buist; Peggy L Porter; Constance Lehman; Stephen H Taplin; Emily White
Journal:  J Natl Cancer Inst       Date:  2004-10-06       Impact factor: 13.506

9.  The relationship of anthropometric measures to radiological features of the breast in premenopausal women.

Authors:  N F Boyd; G A Lockwood; J W Byng; L E Little; M J Yaffe; D L Tritchler
Journal:  Br J Cancer       Date:  1998-11       Impact factor: 7.640

10.  Agreement of mammographic measures of volumetric breast density to MRI.

Authors:  Jeff Wang; Ania Azziz; Bo Fan; Serghei Malkov; Catherine Klifa; David Newitt; Silaja Yitta; Nola Hylton; Karla Kerlikowske; John A Shepherd
Journal:  PLoS One       Date:  2013-12-04       Impact factor: 3.240

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  113 in total

1.  Fully Automated Quantitative Estimation of Volumetric Breast Density from Digital Breast Tomosynthesis Images: Preliminary Results and Comparison with Digital Mammography and MR Imaging.

Authors:  Said Pertuz; Elizabeth S McDonald; Susan P Weinstein; Emily F Conant; Despina Kontos
Journal:  Radiology       Date:  2015-10-21       Impact factor: 11.105

2.  Collaborative Modeling of the Benefits and Harms Associated With Different U.S. Breast Cancer Screening Strategies.

Authors:  Jeanne S Mandelblatt; Natasha K Stout; Clyde B Schechter; Jeroen J van den Broek; Diana L Miglioretti; Martin Krapcho; Amy Trentham-Dietz; Diego Munoz; Sandra J Lee; Donald A Berry; Nicolien T van Ravesteyn; Oguzhan Alagoz; Karla Kerlikowske; Anna N A Tosteson; Aimee M Near; Amanda Hoeffken; Yaojen Chang; Eveline A Heijnsdijk; Gary Chisholm; Xuelin Huang; Hui Huang; Mehmet Ali Ergun; Ronald Gangnon; Brian L Sprague; Sylvia Plevritis; Eric Feuer; Harry J de Koning; Kathleen A Cronin
Journal:  Ann Intern Med       Date:  2016-01-12       Impact factor: 25.391

3.  Trends in Clinical Breast Density Assessment From the Breast Cancer Surveillance Consortium.

Authors:  B L Sprague; K Kerlikowske; E J A Bowles; G H Rauscher; C I Lee; A N A Tosteson; D L Miglioretti
Journal:  J Natl Cancer Inst       Date:  2019-06-01       Impact factor: 13.506

4.  Comparing CISNET Breast Cancer Incidence and Mortality Predictions to Observed Clinical Trial Results of Mammography Screening from Ages 40 to 49.

Authors:  Jeroen J van den Broek; Nicolien T van Ravesteyn; Jeanne S Mandelblatt; Hui Huang; Mehmet Ali Ergun; Elizabeth S Burnside; Cong Xu; Yisheng Li; Oguzhan Alagoz; Sandra J Lee; Natasha K Stout; Juhee Song; Amy Trentham-Dietz; Sylvia K Plevritis; Sue M Moss; Harry J de Koning
Journal:  Med Decis Making       Date:  2018-04       Impact factor: 2.583

5.  Common Model Inputs Used in CISNET Collaborative Breast Cancer Modeling.

Authors:  Jeanne S Mandelblatt; Aimee M Near; Diana L Miglioretti; Diego Munoz; Brian L Sprague; Amy Trentham-Dietz; Ronald Gangnon; Allison W Kurian; Harald Weedon-Fekjaer; Kathleen A Cronin; Sylvia K Plevritis
Journal:  Med Decis Making       Date:  2018-04       Impact factor: 2.583

6.  Using Speed of Sound Imaging to Characterize Breast Density.

Authors:  Mark Sak; Neb Duric; Peter Littrup; Lisa Bey-Knight; Haythem Ali; Patricia Vallieres; Mark E Sherman; Gretchen L Gierach
Journal:  Ultrasound Med Biol       Date:  2016-09-29       Impact factor: 2.998

7.  Circulating Receptor Activator of Nuclear Factor-κB (RANK), RANK ligand (RANKL), and Mammographic Density in Premenopausal Women.

Authors:  Adetunji T Toriola; Catherine M Appleton; Xiaoyu Zong; Jingqin Luo; Katherine Weilbaecher; Rulla M Tamimi; Graham A Colditz
Journal:  Cancer Prev Res (Phila)       Date:  2018-10-23

8.  Milk intake and mammographic density in premenopausal women.

Authors:  Yunan Han; Xiaoyu Zong; Yize Li; Graham A Colditz; Adetunji T Toriola
Journal:  Breast Cancer Res Treat       Date:  2018-11-20       Impact factor: 4.872

9.  The University of Wisconsin Breast Cancer Epidemiology Simulation Model: An Update.

Authors:  Oguzhan Alagoz; Mehmet Ali Ergun; Mucahit Cevik; Brian L Sprague; Dennis G Fryback; Ronald E Gangnon; John M Hampton; Natasha K Stout; Amy Trentham-Dietz
Journal:  Med Decis Making       Date:  2018-04       Impact factor: 2.583

10.  Characterizing the Mammography Technologist Workforce in North Carolina.

Authors:  Louise M Henderson; Mary W Marsh; Thad Benefield; Elizabeth Pearsall; Danielle Durham; Bruce F Schroeder; J Michael Bowling; Cheryl A Viglione; Bonnie C Yankaskas
Journal:  J Am Coll Radiol       Date:  2015-12       Impact factor: 5.532

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