Literature DB >> 27428568

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

Brian L Sprague1, Emily F Conant1, Tracy Onega1, Michael P Garcia1, Elisabeth F Beaber1, Sally D Herschorn1, Constance D Lehman1, Anna N A Tosteson1, Ronilda Lacson1, Mitchell D Schnall1, Despina Kontos1, Jennifer S Haas1, Donald L Weaver1, William E Barlow1.   

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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Year:  2016        PMID: 27428568      PMCID: PMC5050130          DOI: 10.7326/M15-2934

Source DB:  PubMed          Journal:  Ann Intern Med        ISSN: 0003-4819            Impact factor:   25.391


  28 in total

1.  Breast Imaging Reporting and Data System: inter- and intraobserver variability in feature analysis and final assessment.

Authors:  W A Berg; C Campassi; P Langenberg; M J Sexton
Journal:  AJR Am J Roentgenol       Date:  2000-06       Impact factor: 3.959

2.  Impact of the new density reporting laws: radiologist perceptions and actual behavior.

Authors:  David Gur; Amy H Klym; Jill L King; Andriy I Bandos; Jules H Sumkin
Journal:  Acad Radiol       Date:  2015-03-30       Impact factor: 3.173

3.  Mammographic breast density and race.

Authors:  Marcela G del Carmen; Elkan F Halpern; Daniel B Kopans; Beverly Moy; Richard H Moore; Paul E Goss; Kevin S Hughes
Journal:  AJR Am J Roentgenol       Date:  2007-04       Impact factor: 3.959

4.  Misclassification of Breast Imaging Reporting and Data System (BI-RADS) Mammographic Density and Implications for Breast Density Reporting Legislation.

Authors:  Charlotte C Gard; Erin J Aiello Bowles; Diana L Miglioretti; Stephen H Taplin; Carolyn M Rutter
Journal:  Breast J       Date:  2015-07-01       Impact factor: 2.431

5.  Identifying women with dense breasts at high risk for interval cancer: a cohort study.

Authors:  Karla Kerlikowske; Weiwei Zhu; Anna N A Tosteson; Brian L Sprague; Jeffrey A Tice; Constance D Lehman; Diana L Miglioretti
Journal:  Ann Intern Med       Date:  2015-05-19       Impact factor: 25.391

6.  Unifying screening processes within the PROSPR consortium: a conceptual model for breast, cervical, and colorectal cancer screening.

Authors:  Elisabeth F Beaber; Jane J Kim; Marilyn M Schapira; Anna N A Tosteson; Ann G Zauber; Ann M Geiger; Aruna Kamineni; Donald L Weaver; Jasmin A Tiro
Journal:  J Natl Cancer Inst       Date:  2015-05-07       Impact factor: 13.506

7.  Reported mammographic density: film-screen versus digital acquisition.

Authors:  Jennifer A Harvey; Charlotte C Gard; Diana L Miglioretti; Bonnie C Yankaskas; Karla Kerlikowske; Diana S M Buist; Berta A Geller; Tracy L Onega
Journal:  Radiology       Date:  2012-12-18       Impact factor: 11.105

8.  Prevalence of mammographically dense breasts in the United States.

Authors:  Brian L Sprague; Ronald E Gangnon; Veronica Burt; Amy Trentham-Dietz; John M Hampton; Robert D Wellman; Karla Kerlikowske; Diana L Miglioretti
Journal:  J Natl Cancer Inst       Date:  2014-09-12       Impact factor: 13.506

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

Authors:  Jeffrey A Tice; Steven R Cummings; Rebecca Smith-Bindman; Laura Ichikawa; William E Barlow; Karla Kerlikowske
Journal:  Ann Intern Med       Date:  2008-03-04       Impact factor: 25.391

10.  Digital mammographic density and breast cancer risk: a case-control study of six alternative density assessment methods.

Authors:  Amanda Eng; Zoe Gallant; John Shepherd; Valerie McCormack; Jingmei Li; Mitch Dowsett; Sarah Vinnicombe; Steve Allen; Isabel dos-Santos-Silva
Journal:  Breast Cancer Res       Date:  2014-09-20       Impact factor: 6.466

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  47 in total

1.  Automated Breast Density Measurements From Chest Computed Tomography Scans.

Authors:  Touseef A Qureshi; Harini Veeraraghavan; Janice S Sung; Jennifer B Kaplan; Jessica Flynn; Emily S Tonorezos; Suzanne L Wolden; Elizabeth A Morris; Kevin C Oeffinger; Malcolm C Pike; Chaya S Moskowitz
Journal:  J Med Syst       Date:  2019-06-22       Impact factor: 4.460

2.  Breast Density Legislation and the Promise Not Attained.

Authors:  Jennifer S Haas
Journal:  J Gen Intern Med       Date:  2019-02       Impact factor: 5.128

3.  Evaluating Screening Participation, Follow-up, and Outcomes for Breast, Cervical, and Colorectal Cancer in the PROSPR Consortium.

Authors:  William E Barlow; Elisabeth F Beaber; Berta M Geller; Aruna Kamineni; Yingye Zheng; Jennifer S Haas; Chun R Chao; Carolyn M Rutter; Ann G Zauber; Brian L Sprague; Ethan A Halm; Donald L Weaver; Jessica Chubak; V Paul Doria-Rose; Sarah Kobrin; Tracy Onega; Virginia P Quinn; Marilyn M Schapira; Anna N A Tosteson; Douglas A Corley; Celette Sugg Skinner; Mitchell D Schnall; Katrina Armstrong; Cosette M Wheeler; Michael J Silverberg; Bijal A Balasubramanian; Chyke A Doubeni; Dale McLerran; Jasmin A Tiro
Journal:  J Natl Cancer Inst       Date:  2020-03-01       Impact factor: 13.506

Review 4.  Beyond BI-RADS Density: A Call for Quantification in the Breast Imaging Clinic.

Authors:  Emily F Conant; Brian L Sprague; Despina Kontos
Journal:  Radiology       Date:  2018-02       Impact factor: 11.105

5.  Harnessing the Power of Deep Learning to Assess Breast Cancer Risk.

Authors:  Manisha Bahl
Journal:  Radiology       Date:  2019-12-17       Impact factor: 11.105

6.  A new automated method to evaluate 2D mammographic breast density according to BI-RADS® Atlas Fifth Edition recommendations.

Authors:  Corinne Balleyguier; Julia Arfi-Rouche; Bruno Boyer; Emilien Gauthier; Valerie Helin; Ara Loshkajian; Stephane Ragusa; Suzette Delaloge
Journal:  Eur Radiol       Date:  2019-02-15       Impact factor: 5.315

7.  Agreement between Breast Percentage Density Estimations from Standard-Dose versus Synthetic Digital Mammograms: Results from a Large Screening Cohort Using Automated Measures.

Authors:  Emily F Conant; Brad M Keller; Lauren Pantalone; Aimilia Gastounioti; Elizabeth S McDonald; Despina Kontos
Journal:  Radiology       Date:  2017-01-25       Impact factor: 11.105

Review 8.  Risk-based Breast Cancer Screening: Implications of Breast Density.

Authors:  Christoph I Lee; Linda E Chen; Joann G Elmore
Journal:  Med Clin North Am       Date:  2017-07       Impact factor: 5.456

Review 9.  Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives.

Authors:  Krzysztof J Geras; Ritse M Mann; Linda Moy
Journal:  Radiology       Date:  2019-09-24       Impact factor: 11.105

10.  Automated and Clinical Breast Imaging Reporting and Data System Density Measures Predict Risk for Screen-Detected and Interval Cancers: A Case-Control Study.

Authors:  Karla Kerlikowske; Christopher G Scott; Amir P Mahmoudzadeh; Lin Ma; Stacey Winham; Matthew R Jensen; Fang Fang Wu; Serghei Malkov; V Shane Pankratz; Steven R Cummings; John A Shepherd; Kathleen R Brandt; Diana L Miglioretti; Celine M Vachon
Journal:  Ann Intern Med       Date:  2018-05-01       Impact factor: 25.391

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