Variation in Mammographic Breast Density Assessments Among Radiologists in Clinical Practice: A Multicenter Observational Study.

Background About half of the United States has legislation requiring radiology facilities to disclose mammographic breast density information to women, often with language recommending discussion of supplemental screening options for women with dense breasts. Objective To examine variation in breast density assessment across radiologists in clinical practice. Design Cross-sectional and longitudinal analyses of prospectively collected observational data. Setting 30 radiology facilities within the 3 breast cancer screening research centers of the Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) consortium. Participants Radiologists who interpreted at least 500 screening mammograms during 2011 to 2013 (n = 83). Data on 216 783 screening mammograms from 145 123 women aged 40 to 89 years were included. Measurements Mammographic breast density, as clinically recorded using the 4 Breast Imaging Reporting and Data System categories (heterogeneously dense and extremely dense categories were considered "dense" for analyses), and patient age, race, and body mass index (BMI). Results Overall, 36.9% of mammograms were rated as showing dense breasts. Across radiologists, this percentage ranged from 6.3% to 84.5% (median, 38.7% [interquartile range, 28.9% to 50.9%]), with multivariable adjustment for patient characteristics having little effect (interquartile range, 29.9% to 50.8%). Examination of patient subgroups revealed that variation in density assessment across radiologists was pervasive in all but the most extreme patient age and BMI combinations. Among women with consecutive mammograms interpreted by different radiologists, 17.2% (5909 of 34 271) had discordant assessments of dense versus nondense status. Limitation Quantitative measures of mammographic breast density were not available for comparison. Conclusion There is wide variation in density assessment across radiologists that should be carefully considered by providers and policymakers when considering supplemental screening strategies. The likelihood of a woman being told she has dense breasts varies substantially according to which radiologist interprets her mammogram. Primary Funding Source National Institutes of Health.

[1]  N. Boyd,et al.  Mammographic density and the risk and detection of breast cancer. , 2007, The New England journal of medicine.

[2]  Impact of the new density reporting laws: radiologist perceptions and actual behavior. , 2015, Academic radiology.

[3]  Jingmei Li,et al.  Digital mammographic density and breast cancer risk: a case–control study of six alternative density assessment methods , 2014, Breast Cancer Research.

[4]  Wenda He,et al.  A Review on Automatic Mammographic Density and Parenchymal Segmentation , 2015, International journal of breast cancer.

[5]  P. Langenberg,et al.  Breast Imaging Reporting and Data System: inter- and intraobserver variability in feature analysis and final assessment. , 2000, AJR. American journal of roentgenology.

[6]  C. Lehman,et al.  Identifying women with dense breasts at high risk for interval cancer: a cohort study. , 2015, Annals of internal medicine.

[7]  Bonnie N Joe,et al.  Breast density legislation: mandatory disclosure to patients, alternative screening, billing, reimbursement. , 2015, AJR. American journal of roentgenology.

[8]  L. Kolonel,et al.  A Longitudinal Investigation of Mammographic Density: The Multiethnic Cohort , 2006, Cancer Epidemiology Biomarkers & Prevention.

[9]  D. Miglioretti,et al.  Misclassification of Breast Imaging Reporting and Data System (BI‐RADS) Mammographic Density and Implications for Breast Density Reporting Legislation , 2015, The breast journal.

[10]  Karla Kerlikowske,et al.  Using Clinical Factors and Mammographic Breast Density to Estimate Breast Cancer Risk: Development and Validation of a New Predictive Model , 2008, Annals of Internal Medicine.

[11]  A D Mickalide,et al.  U.S. Preventive Services Task Force. , 1986, Pediatric clinics of North America.

[12]  M. Gail,et al.  Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. , 1989, Journal of the National Cancer Institute.

[13]  D. Miglioretti,et al.  Individual and Combined Effects of Age, Breast Density, and Hormone Replacement Therapy Use on the Accuracy of Screening Mammography , 2003, Annals of Internal Medicine.

[14]  J. Haas,et al.  The Divide Between Breast Density Notification Laws and Evidence-Based Guidelines for Breast Cancer Screening: Legislating Practice. , 2015, JAMA internal medicine.

[15]  D. Miglioretti,et al.  Reported mammographic density: film-screen versus digital acquisition. , 2013, Radiology.

[16]  R. Carlos,et al.  Dense breast legislation in the United States: state of the states. , 2013, Journal of the American College of Radiology : JACR.

[17]  Ammarin Thakkinstian,et al.  Risk prediction models of breast cancer: a systematic review of model performances , 2012, Breast Cancer Research and Treatment.

[18]  Karla Kerlikowske,et al.  Performance benchmarks for screening mammography. , 2006, Radiology.

[19]  Olivier Alonzo-Proulx,et al.  Reliability of automated breast density measurements. , 2015, Radiology.

[20]  K. Kerlikowske,et al.  The Effect of Change in Body Mass Index on Volumetric Measures of Mammographic Density , 2015, Cancer Epidemiology, Biomarkers & Prevention.

[21]  T. Sellers,et al.  Association of mammographically defined percent breast density with epidemiologic risk factors for breast cancer (United States) , 2000, Cancer Causes & Control.

[22]  N. Boyd,et al.  Breast tissue composition and susceptibility to breast cancer. , 2010, Journal of the National Cancer Institute.

[23]  Diana L Miglioretti,et al.  Reproducibility of BI‐RADS Breast Density Measures Among Community Radiologists: A Prospective Cohort Study , 2012, The breast journal.

[24]  Donald L Weaver,et al.  Unifying screening processes within the PROSPR consortium: a conceptual model for breast, cervical, and colorectal cancer screening. , 2015, Journal of the National Cancer Institute.

[25]  E L Korn,et al.  Predictive Margins with Survey Data , 1999, Biometrics.

[26]  T. Wilt,et al.  Screening for breast cancer: U.S. Preventive Services Task Force recommendation statement. , 2009, Annals of internal medicine.

[27]  E. Halpern,et al.  Mammographic breast density and race. , 2007, AJR. American journal of roentgenology.

[28]  P. Narula MAMMOGRAPHIC DENSITY AND THE RISK AND DETECTION OF BREAST CANCER , 2016 .

[29]  Karla Kerlikowske,et al.  Prevalence of mammographically dense breasts in the United States. , 2014, Journal of the National Cancer Institute.

[30]  K. Kerlikowske,et al.  Variability and accuracy in mammographic interpretation using the American College of Radiology Breast Imaging Reporting and Data System. , 1998, Journal of the National Cancer Institute.