ReviewBreast cancer research and treatment2010
Predictors of interobserver agreement in breast imaging using the Breast Imaging Reporting and Data System.
Review in Breast cancer research and treatment, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Considerations for Evaluating the Introduction of New Cancer Screening Technology: Use of Interval Cancers to Assess Potential Benefits and Harms.International journal of environmental research and public health · 2022Review
- Feasibility study using multifocal Doppler twinkling artifacts to detect suspicious microcalcifications in ex vivo specimens of breast cancer on US.Scientific reports · 2022Article
- Development and validation of a deep learning model for detection of breast cancers in mammography from multi-institutional datasets.PloS one · 2022Article
- Measuring intrarater association between correlated ordinal ratings.Biometrical journal. Biometrische Zeitschrift · 2020Article
- Intercountry analysis of breast density classification using visual grading.The British journal of radiology · 2017Article
- Breast Imaging in the Era of Big Data: Structured Reporting and Data Mining.AJR. American journal of roentgenology · 2016Article
- Mammographic features of calcifications in DCIS: correlation with oestrogen receptor and human epidermal growth factor receptor 2 status.European radiology · 2013Article
- Radiologist agreement for mammographic recall by case difficulty and finding type.Journal of the American College of Radiology : JACR · 2012Article
- Computer-aided classification of breast masses: performance and interobserver variability of expert radiologists versus residents.Radiology · 2011Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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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Registered trials
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