Variation in Mammographic Breast Density Assessments Among Radiologists in Clinical Practice: A Multicenter Observational Study
Publication: Annals of Internal Medicine
Volume 165, Number 7
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Abstract
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.
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References
1.
Carney PA, Miglioretti DL, Yankaskas BC, Kerlikowske K, Rosenberg R, Rutter CM, et al. Individual and combined effects of age, breast density, and hormone replacement therapy use on the accuracy of screening mammography. Ann Intern Med. 2003;138:168-75. [PMID: 12558355]. doi: 10.7326/0003-4819-138-3-200302040-00008
2.
Boyd NF, Guo H, Martin LJ, Sun L, Stone J, Fishell E, et al. Mammographic density and the risk and detection of breast cancer. N Engl J Med. 2007;356:227-36. [PMID: 17229950]
3.
Are You Dense Advocacy. 24 density reporting bills and a growing number of introduced bills in 2016. 1 February 2016. Accessed at http://areyoudenseadvocacy.org/worxcms_published/news_page200.shtml on 16 March 2016.
4.
Dehkordy SF, Carlos RC. Dense breast legislation in the United States: state of the states. J Am Coll Radiol. 2013;10:899-902. [PMID: 24295937] doi: 10.1016/j.jacr.2013.09.007
5.
Ray KM, Price ER, Joe BN. Breast density legislation: mandatory disclosure to patients, alternative screening, billing, reimbursement. AJR Am J Roentgenol. 2015;204:257-60. [PMID: 25615746] doi: 10.2214/AJR.14.13558
6.
Breast Density and Mammography Reporting Act of 2015, § 370 (2015).
7.
U.S. Food and Drug Administration. Mammography Quality Standards Act; regulatory amendments. Fed Reg. 2015;80:35020.
8.
Sprague BL, Gangnon RE, Burt V, Trentham-Dietz A, Hampton JM, Wellman RD, et al. Prevalence of mammographically dense breasts in the United States. J Natl Cancer Inst. 2014;106. [PMID: 25217577] doi: 10.1093/jnci/dju255
9.
Siu AL; U.S. Preventive Services Task Force. Screening for breast cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med. 2016;164:279-96. [PMID: 26757170]. doi: 10.7326/M15-2886
10.
Haas JS, Kaplan CP. The divide between breast density notification laws and evidence-based guidelines for breast cancer screening: legislating practice. JAMA Intern Med. 2015;175:1439-40. [PMID: 26147642] doi: 10.1001/jamainternmed.2015.3040
11.
American College of Radiology. ACR BI-RADS Atlas—Mammography. 4th ed. Reston, VA: American College of Radiology; 2003.
12.
American College of Radiology. ACR BI-RADS Atlas—Mammography. 5th ed. Reston, VA: American College of Radiology; 2013.
13.
Kerlikowske K, Grady D, Barclay J, Frankel SD, Ominsky SH, Sickles EA, et al. Variability and accuracy in mammographic interpretation using the American College of Radiology Breast Imaging Reporting and Data System. J Natl Cancer Inst. 1998;90:1801-9. [PMID: 9839520]
14.
Berg WA, Campassi C, Langenberg P, Sexton MJ. Breast Imaging Reporting and Data System: inter- and intraobserver variability in feature analysis and final assessment. AJR Am J Roentgenol. 2000;174:1769-77. [PMID: 10845521]
15.
Spayne MC, Gard CC, Skelly J, Miglioretti DL, Vacek PM, Geller BM. Reproducibility of BI-RADS breast density measures among community radiologists: a prospective cohort study. Breast J. 2012;18:326-33. [PMID: 22607064] doi: 10.1111/j.1524-4741.2012.01250.x
16.
Gard CC, Aiello Bowles EJ, Miglioretti DL, Taplin SH, Rutter CM. Misclassification of Breast Imaging Reporting and Data System (BI-RADS) mammographic density and implications for breast density reporting legislation. Breast J. 2015;21:481-9. [PMID: 26133090] doi: 10.1111/tbj.12443
17.
Harvey JA, Gard CC, Miglioretti DL, Yankaskas BC, Kerlikowske K, Buist DS, et al; Breast Cancer Surveillance Consortium. Reported mammographic density: film-screen versus digital acquisition. Radiology. 2013;266:752-8. [PMID: 23249570] doi: 10.1148/radiol.12120221
18.
Beaber EF, Kim JJ, Schapira MM, Tosteson AN, Zauber AG, Geiger AM, et al; Population-based Research Optimizing Screening through Personalized Regimens Consortium. Unifying screening processes within the PROSPR consortium: a conceptual model for breast, cervical, and colorectal cancer screening. J Natl Cancer Inst. 2015;107:djv120. [PMID: 25957378] doi: 10.1093/jnci/djv120
19.
Graubard BI, Korn EL. Predictive margins with survey data. Biometrics. 1999;55:652-9. [PMID: 11318229]
20.
Kerlikowske K, Zhu W, Tosteson AN, Sprague BL, Tice JA, Lehman CD, et al; Breast Cancer Surveillance Consortium. Identifying women with dense breasts at high risk for interval cancer: a cohort study. Ann Intern Med. 2015;162:673-81. [PMID: 25984843]. doi: 10.7326/M14-1465
21.
Tice JA, Cummings SR, Smith-Bindman R, Ichikawa L, Barlow WE, Kerlikowske K. Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model. Ann Intern Med. 2008;148:337-47. [PMID: 18316752]. doi: 10.7326/0003-4819-148-5-200803040-00004
22.
Gail MH, Brinton LA, Byar DP, Corle DK, Green SB, Schairer C, et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst. 1989;81:1879-86. [PMID: 2593165]
23.
Anothaisintawee T, Teerawattananon Y, Wiratkapun C, Kasamesup V, Thakkinstian A. Risk prediction models of breast cancer: a systematic review of model performances. Breast Cancer Res Treat. 2012;133:1-10. [PMID: 22076477] doi: 10.1007/s10549-011-1853-z
24.
Maskarinec G, Pagano I, Lurie G, Kolonel LN. A longitudinal investigation of mammographic density: the multiethnic cohort. Cancer Epidemiol Biomarkers Prev. 2006;15:732-9. [PMID: 16614116]
25.
Hart V, Reeves KW, Sturgeon SR, Reich NG, Sievert LL, Kerlikowske K, et al. The effect of change in body mass index on volumetric measures of mammographic density. Cancer Epidemiol Biomarkers Prev. 2015;24:1724-30. [PMID: 26315554] doi: 10.1158/1055-9965.EPI-15-0330
26.
American College of Radiology. ACR statement on reporting breast density in mammography reports and patient summaries. 24 April 2012. Accessed at www.acr.org/About-Us/Media-Center/Position-Statements/Position-Statements-Folder/Statement-on-Reporting-Breast-Density-in-Mammography-Reports-and-Patient-Summaries on 16 March 2016.
27.
He W, Juette A, Denton ER, Oliver A, Martí R, Zwiggelaar R. A review on automatic mammographic density and parenchymal segmentation. Int J Breast Cancer. 2015;2015:276217. [PMID: 26171249] doi: 10.1155/2015/276217
28.
Alonzo-Proulx O, Mawdsley GE, Patrie JT, Yaffe MJ, Harvey JA. Reliability of automated breast density measurements. Radiology. 2015;275:366-76. [PMID: 25734553] doi: 10.1148/radiol.15141686
29.
Gur D, Klym AH, King JL, Bandos AI, Sumkin JH. Impact of the new density reporting laws: radiologist perceptions and actual behavior. Acad Radiol. 2015;22:679-83. [PMID: 25837723] doi: 10.1016/j.acra.2015.02.009
30.
National Center for Health Statistics. Health, United States, 2014. Hyattsville, MD: National Center for Health Statistics; 2015.
31.
Boyd NF, Martin LJ, Bronskill M, Yaffe MJ, Duric N, Minkin S. Breast tissue composition and susceptibility to breast cancer. J Natl Cancer Inst. 2010;102:1224-37. [PMID: 20616353] doi: 10.1093/jnci/djq239
32.
del Carmen MG, Halpern EF, Kopans DB, Moy B, Moore RH, Goss PE, et al. Mammographic breast density and race. AJR Am J Roentgenol. 2007;188:1147-50. [PMID: 17377060]
33.
Vachon CM, Kuni CC, Anderson K, Anderson VE, Sellers TA. Association of mammographically defined percent breast density with epidemiologic risk factors for breast cancer (United States). Cancer Causes Control. 2000;11:653-62. [PMID: 10977110]
34.
Rosenberg RD, Yankaskas BC, Abraham LA, Sickles EA, Lehman CD, Geller BM, et al. Performance benchmarks for screening mammography. Radiology. 2006;241:55-66. [PMID: 16990671]
Information & Authors
Information
Published In
Annals of Internal Medicine
Volume 165 • Number 7 • 4 October 2016
Pages: 457 - 464
History
Published online: 19 July 2016
Published in issue: 4 October 2016
Keywords
Copyright
© 2016 American College of Physicians.
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Variation in Mammographic Breast Density Assessments Among Radiologists in Clinical Practice: A Multicenter Observational Study. Ann Intern Med.2016;165:457-464. [Epub 19 July 2016]. doi:10.7326/M15-2934
Variation in Mammographic Breast Density Assessments Among Radiologists in Clinical Practice: A Multicenter Observational Study. Ann Intern Med.2016;165:457-464. [Epub 19 July 2016]. doi:10.7326/M15-2934
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