Literature DB >> 33180237

Potential of using mammography screening appointments to identify high-risk women: cross sectional survey results from the national health interview survey.

Anand K Narayan1, Sarah F Mercaldo2, Yasha P Gupta3, Erica T Warner2, Constance D Lehman2, Randy C Miles2.   

Abstract

PURPOSE: Mammography screening encounters may represent ideal opportunities to identify high-risk women for risk-based screening. During mammography appointments, radiology practices evaluate breast density and ascertain known breast cancer risk factors. Our purpose was to evaluate the potential for mammographic screening encounters to identify high-risk women by estimating the (1) proportion of high-risk women who report that they have undergone mammographic screening and the (2) proportion of high-risk women who receive recommendations for breast MRI screening.
METHODS: Women ages 30-85 without breast cancer histories were included from the 2015 National Health Interview Survey, a nationally representative cross-sectional household survey (response rate 80%). Breast Cancer Risk Assessment Tool was used to determine high-risk (lifetime risk>20%). Among high-risk women, primary outcome was proportion reporting mammography screening, secondary outcome was receipt of a breast MRI recommendation after recent mammogram, accounting for complex survey design.
RESULTS: 14,958 women were included. 1.0% were high-risk of whom: 91.9% ever had a mammogram, 68.0% had a mammogram within the last year, 81.5% had a mammogram within the last 2 years. 6.4% were recommended to undergo breast MRI. Among high-risk women, women with dense breast tissue were more likely (OR 496.0, 95%CI 52.6,4674.0) and older women were less likely (OR 0.91, 95%CI 0.84,0.99) to receive recommendations for breast MRI.
CONCLUSIONS: Among high-risk women, 92% reported undergoing at least one mammogram in their lives. 94% did not receive recommendations for breast MRI screening and 32% did not have a mammogram within the last year. To identify high-risk women, breast imaging centers should consider determining lifetime breast cancer risk during mammography screening visits.

Entities:  

Keywords:  Breast neoplasms; Chemoprevention; Epidemiology; Magnetic resonance imaging; Mammography; Risk assessment

Mesh:

Year:  2020        PMID: 33180237     DOI: 10.1007/s10549-020-06002-9

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


  14 in total

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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

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

3.  Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration.

Authors:  Jan P Vandenbroucke; Erik von Elm; Douglas G Altman; Peter C Gøtzsche; Cynthia D Mulrow; Stuart J Pocock; Charles Poole; James J Schlesselman; Matthias Egger
Journal:  Epidemiology       Date:  2007-11       Impact factor: 4.822

4.  American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography.

Authors:  Debbie Saslow; Carla Boetes; Wylie Burke; Steven Harms; Martin O Leach; Constance D Lehman; Elizabeth Morris; Etta Pisano; Mitchell Schnall; Stephen Sener; Robert A Smith; Ellen Warner; Martin Yaffe; Kimberly S Andrews; Christy A Russell
Journal:  CA Cancer J Clin       Date:  2007 Mar-Apr       Impact factor: 508.702

5.  Nationwide cross-sectional adherence to mammography screening guidelines: national behavioral risk factor surveillance system survey results.

Authors:  Anand Narayan; Alexander Fischer; Zihe Zhang; Ryan Woods; Elizabeth Morris; Susan Harvey
Journal:  Breast Cancer Res Treat       Date:  2017-05-15       Impact factor: 4.872

6.  Prospective approach to breast cancer risk prediction in African American women: the black women's health study model.

Authors:  Deborah A Boggs; Lynn Rosenberg; Lucile L Adams-Campbell; Julie R Palmer
Journal:  J Clin Oncol       Date:  2015-01-26       Impact factor: 44.544

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

Authors:  Jeffrey A Tice; Steven R Cummings; Elad Ziv; Karla Kerlikowske
Journal:  Breast Cancer Res Treat       Date:  2005-11       Impact factor: 4.872

8.  A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction.

Authors:  Adam Yala; Constance Lehman; Tal Schuster; Tally Portnoi; Regina Barzilay
Journal:  Radiology       Date:  2019-05-07       Impact factor: 11.105

9.  Patterns of breast magnetic resonance imaging use in community practice.

Authors:  Karen J Wernli; Wendy B DeMartini; Laura Ichikawa; Constance D Lehman; Tracy Onega; Karla Kerlikowske; Louise M Henderson; Berta M Geller; Mike Hofmann; Bonnie C Yankaskas
Journal:  JAMA Intern Med       Date:  2014-01       Impact factor: 21.873

10.  Predictors of BRCA1/2 genetic testing among Black women with breast cancer: a population-based study.

Authors:  Tarsha Jones; Anne Marie McCarthy; Younji Kim; Katrina Armstrong
Journal:  Cancer Med       Date:  2017-06-19       Impact factor: 4.452

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  1 in total

Review 1.  Assessing Risk of Breast Cancer: A Review of Risk Prediction Models.

Authors:  Geunwon Kim; Manisha Bahl
Journal:  J Breast Imaging       Date:  2021-02-19
  1 in total

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