Literature DB >> 20300960

Predictors of interobserver agreement in breast imaging using the Breast Imaging Reporting and Data System.

Anna Liza M Antonio1, Catherine M Crespi.   

Abstract

The Breast Imaging Reporting and Data System (BI-RADS) was introduced in 1993 to standardize the interpretation of mammograms. Though many studies have assessed the validity of the system, fewer have examined its reliability. Our objective is to identify predictors of reliability as measured by the kappa statistic. We identified studies conducted between 1993 and 2009 which reported kappa values for interpreting mammograms using any edition of BI-RADS. Bivariate and multivariate multilevel analyses were used to examine associations between potential predictors and kappa values. We identified ten eligible studies, which yielded 88 kappa values for the analysis. Potential predictors of kappa included: whether or not the study included negative cases, whether single- or two-view mammograms were used, whether or not mammograms were digital versus screen-film, whether or not the fourth edition of BI-RADS was utilized, the BI-RADS category being evaluated, whether or not readers were trained, whether or not there was an overlap in readers' professional activities, the number of cases in the study and the country in which the study was conducted. Our best multivariate model identified training, use of two-view mammograms and BI-RADS categories (masses, calcifications, and final assessments) as predictors of kappa. Training, use of two-view mammograms and focusing on mass description may be useful in increasing reliability in mammogram interpretation. Calcification and final assessment descriptors are areas for potential improvement. These findings are important for implementing policies in BI-RADS use before introducing the system in different settings and improving current implementations.

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Year:  2010        PMID: 20300960      PMCID: PMC2843585          DOI: 10.1007/s10549-010-0770-x

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


  36 in total

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3.  Breast cancer CADx based on BI-RAds descriptors from two mammographic views.

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4.  Real-time sonoelastography performed in addition to B-mode ultrasound and mammography: improved differentiation of breast lesions?

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5.  Positive predictive value of breast cancer in the lesions categorized as BI-RADS category 5.

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6.  [Second reading of breast imaging at the hospital department of radiology: reasonable or waste of money?].

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7.  Mammographic density measured with quantitative computer-aided method: comparison with radiologists' estimates and BI-RADS categories.

Authors:  Katherine E Martin; Mark A Helvie; Chuan Zhou; Marilyn A Roubidoux; Janet E Bailey; Chintana Paramagul; Caroline E Blane; Katherine A Klein; Seema S Sonnad; Heang-Ping Chan
Journal:  Radiology       Date:  2006-07-20       Impact factor: 11.105

8.  Real-time elastography--an advanced method of ultrasound: First results in 108 patients with breast lesions.

Authors:  A Thomas; T Fischer; H Frey; R Ohlinger; S Grunwald; J-U Blohmer; K-J Winzer; S Weber; G Kristiansen; B Ebert; S Kümmel
Journal:  Ultrasound Obstet Gynecol       Date:  2006-09       Impact factor: 7.299

9.  Randomized trial of screen-film versus full-field digital mammography with soft-copy reading in population-based screening program: follow-up and final results of Oslo II study.

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Journal:  Radiology       Date:  2007-09       Impact factor: 11.105

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Authors:  Thomas Caramella; Claire Chapellier; Francette Ettore; Ines Raoust; Emmanuel Chamorey; Catherine Balu-Maestro
Journal:  Clin Imaging       Date:  2007 May-Jun       Impact factor: 1.605

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  8 in total

1.  Computer-aided classification of breast masses: performance and interobserver variability of expert radiologists versus residents.

Authors:  Swatee Singh; Jeff Maxwell; Jay A Baker; Jennifer L Nicholas; Joseph Y Lo
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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.  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

4.  Radiologist agreement for mammographic recall by case difficulty and finding type.

Authors:  Tracy Onega; Megan Smith; Diana L Miglioretti; Patricia A Carney; Berta A Geller; Karla Kerlikowske; Diana S M Buist; Robert D Rosenberg; Robert A Smith; Edward A Sickles; Sebastien Haneuse; Melissa L Anderson; Bonnie Yankaskas
Journal:  J Am Coll Radiol       Date:  2012-11       Impact factor: 5.532

5.  Mammographic features of calcifications in DCIS: correlation with oestrogen receptor and human epidermal growth factor receptor 2 status.

Authors:  Min Sun Bae; Woo Kyung Moon; Jung Min Chang; Nariya Cho; So Yeon Park; Jae-Kyung Won; Yoon-Kyung Jeon; Hyeong-Gon Moon; Wonshik Han; In Ae Park
Journal:  Eur Radiol       Date:  2013-03-20       Impact factor: 5.315

6.  Breast Imaging in the Era of Big Data: Structured Reporting and Data Mining.

Authors:  Laurie R Margolies; Gaurav Pandey; Eliot R Horowitz; David S Mendelson
Journal:  AJR Am J Roentgenol       Date:  2015-11-20       Impact factor: 3.959

7.  Feasibility study using multifocal Doppler twinkling artifacts to detect suspicious microcalcifications in ex vivo specimens of breast cancer on US.

Authors:  Vivian Youngjean Park; Jinbum Kang; Kanghee Han; Ilseob Song; Kang-Sik Kim; Se Jin Nam; Ga Ram Kim; Jung Hyun Yoon; Won Seuk Jang; Yangmo Yoo; Min Jung Kim
Journal:  Sci Rep       Date:  2022-02-21       Impact factor: 4.379

8.  Development and validation of a deep learning model for detection of breast cancers in mammography from multi-institutional datasets.

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Journal:  PLoS One       Date:  2022-03-24       Impact factor: 3.240

  8 in total

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