Literature DB >> 26133090

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

Charlotte C Gard1, Erin J Aiello Bowles2, Diana L Miglioretti2,3, Stephen H Taplin4, Carolyn M Rutter2,5.   

Abstract

USA states have begun legislating mammographic breast density reporting to women, requiring that women undergoing screening mammography who have dense breast tissue (Breast Imaging Reporting and Data System [BI-RADS] density c or d) receive written notification of their breast density; however, the impact that misclassification of breast density will have on this reporting remains unclear. The aim of this study was to assess reproducibility of the four-category BI-RADS density measure and examine its relationship with a continuous measure of percent density. We enrolled 19 radiologists, experienced in breast imaging, from a single integrated health care system. Radiologists interpreted 341 screening mammograms at two points in time 6 months apart. We assessed intra- and interobserver agreement in radiologists'; interpretations of BI-RADS density and explored whether agreement depended upon radiologist characteristics. We examined the relationship between BI-RADS density and percent density in a subset of 282 examinations. Intraradiologist agreement was moderate to substantial, with kappa varying across radiologists from 0.50 to 0.81 (mean = 0.69, 95% CI [0.63, 0.73]). Intraradiologist agreement was higher for radiologists with ≥10 years experience interpreting mammograms (difference in mean kappa = 0.10, 95% CI [0.01, 0.24]). Interradiologist agreement varied widely across radiologist pairs from slight to substantial, with kappa ranging from 0.02 to 0.72 (mean = 0.46, 95% CI [0.36, 0.55]). Of 145 examinations interpreted as "nondense" (BI-RADS density a or b) by the majority of radiologists, 82.8% were interpreted as "dense" (BI-RADS density c or d) by at least one radiologist. Of 187 examinations interpreted as "dense" by the majority of radiologists, 47.1% were interpreted as "nondense" by at least one radiologist. While the examinations of almost half of the women in our study were interpreted clinically as having BI-RADS density c or d, only about 10% of examinations had percent density >50%. Our results suggest that breast density reporting based on a single BI-RADS density interpretation may be misleading due to high interradiologist variability and a lack of correspondence between BI-RADS density and percent density.
© 2015 Wiley Periodicals, Inc.

Entities:  

Keywords:  BI-RADS density; breast density reporting legislation; intra- and interradiologist agreement; misclassification; percent density

Mesh:

Year:  2015        PMID: 26133090      PMCID: PMC4558212          DOI: 10.1111/tbj.12443

Source DB:  PubMed          Journal:  Breast J        ISSN: 1075-122X            Impact factor:   2.431


  21 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.  Radiologist assessment of breast density by BI-RADS categories versus fully automated volumetric assessment.

Authors:  Hye Mi Gweon; Ji Hyun Youk; Jeong-Ah Kim; Eun Ju Son
Journal:  AJR Am J Roentgenol       Date:  2013-09       Impact factor: 3.959

3.  The measurement of observer agreement for categorical data.

Authors:  J R Landis; G G Koch
Journal:  Biometrics       Date:  1977-03       Impact factor: 2.571

4.  Breast cancer risk and measured mammographic density.

Authors:  M J Yaffe; N F Boyd; J W Byng; R A Jong; E Fishell; G A Lockwood; L E Little; D L Tritchler
Journal:  Eur J Cancer Prev       Date:  1998-02       Impact factor: 2.497

5.  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

6.  Variability in interpretive performance at screening mammography and radiologists' characteristics associated with accuracy.

Authors:  Joann G Elmore; Sara L Jackson; Linn Abraham; Diana L Miglioretti; Patricia A Carney; Berta M Geller; Bonnie C Yankaskas; Karla Kerlikowske; Tracy Onega; Robert D Rosenberg; Edward A Sickles; Diana S M Buist
Journal:  Radiology       Date:  2009-10-28       Impact factor: 11.105

7.  Individual and combined effects of age, breast density, and hormone replacement therapy use on the accuracy of screening mammography.

Authors:  Patricia A Carney; Diana L Miglioretti; Bonnie C Yankaskas; Karla Kerlikowske; Robert Rosenberg; Carolyn M Rutter; Berta M Geller; Linn A Abraham; Steven H Taplin; Mark Dignan; Gary Cutter; Rachel Ballard-Barbash
Journal:  Ann Intern Med       Date:  2003-02-04       Impact factor: 25.391

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.  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

Review 10.  Mammographic density. Measurement of mammographic density.

Authors:  Martin J Yaffe
Journal:  Breast Cancer Res       Date:  2008-06-19       Impact factor: 6.466

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  27 in total

1.  Breast density across a regional screening population: effects of age, ethnicity and deprivation.

Authors:  Samantha L Heller; Sue Hudson; Louise S Wilkinson
Journal:  Br J Radiol       Date:  2015-09-02       Impact factor: 3.039

2.  Measuring intrarater association between correlated ordinal ratings.

Authors:  Kerrie P Nelson; Thomas J Zhou; Don Edwards
Journal:  Biom J       Date:  2020-06-11       Impact factor: 2.207

3.  Assessing the influence of rater and subject characteristics on measures of agreement for ordinal ratings.

Authors:  Kerrie P Nelson; Aya A Mitani; Don Edwards
Journal:  Stat Med       Date:  2017-06-13       Impact factor: 2.373

4.  Intercountry analysis of breast density classification using visual grading.

Authors:  Christine N Damases; Peter Hogg; Mark F McEntee
Journal:  Br J Radiol       Date:  2017-06-14       Impact factor: 3.039

Review 5.  Supplemental Screening for Breast Cancer in Women With Dense Breasts: A Systematic Review for the U.S. Preventive Services Task Force.

Authors:  Joy Melnikow; Joshua J Fenton; Evelyn P Whitlock; Diana L Miglioretti; Meghan S Weyrich; Jamie H Thompson; Kunal Shah
Journal:  Ann Intern Med       Date:  2016-01-12       Impact factor: 25.391

6.  One versus Two Breast Density Measures to Predict 5- and 10-Year Breast Cancer Risk.

Authors:  Karla Kerlikowske; Charlotte C Gard; Brian L Sprague; Jeffrey A Tice; Diana L Miglioretti
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2015-03-30       Impact factor: 4.254

7.  A paired kappa to compare binary ratings across two medical tests.

Authors:  Kerrie P Nelson; Don Edwards
Journal:  Stat Med       Date:  2019-05-17       Impact factor: 2.373

Review 8.  Review of quantitative multiscale imaging of breast cancer.

Authors:  Michael A Pinkert; Lonie R Salkowski; Patricia J Keely; Timothy J Hall; Walter F Block; Kevin W Eliceiri
Journal:  J Med Imaging (Bellingham)       Date:  2018-01-22

9.  Racial Differences in Quantitative Measures of Area and Volumetric Breast Density.

Authors:  Anne Marie McCarthy; Brad M Keller; Lauren M Pantalone; Meng-Kang Hsieh; Marie Synnestvedt; Emily F Conant; Katrina Armstrong; Despina Kontos
Journal:  J Natl Cancer Inst       Date:  2016-04-29       Impact factor: 13.506

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

Authors:  Brian L Sprague; Emily F Conant; Tracy Onega; Michael P Garcia; Elisabeth F Beaber; Sally D Herschorn; Constance D Lehman; Anna N A Tosteson; Ronilda Lacson; Mitchell D Schnall; Despina Kontos; Jennifer S Haas; Donald L Weaver; William E Barlow
Journal:  Ann Intern Med       Date:  2016-07-19       Impact factor: 25.391

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