Department of Biostatistics, UCLA School of Public Health, University of California, Los Angeles, CA, 90095-1776, USA.
Breast Cancer Res Treat. 2010 Apr;120(3):539-46. doi: 10.1007/s10549-010-0770-x. Epub 2010 Feb 21.
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.
乳腺影像报告和数据系统（BI-RADS）于 1993 年推出，旨在规范乳腺 X 线摄影的解读。尽管许多研究评估了该系统的有效性，但很少有研究检查其可靠性。我们的目的是确定以κ统计量衡量的可靠性的预测因素。我们确定了在 1993 年至 2009 年间进行的研究，这些研究报告了使用任何版本的 BI-RADS 解读乳腺 X 线摄影的κ值。使用二元和多元多层分析来检查潜在预测因素与κ值之间的关联。我们确定了十项符合条件的研究，这些研究得出了 88 个κ值进行分析。κ的潜在预测因素包括：研究是否包括阴性病例、使用单视图或双视图乳腺 X 线摄影、乳腺 X 线摄影是否为数字与屏片、是否使用第四版 BI-RADS、正在评估的 BI-RADS 类别、读者是否接受过培训、读者的专业活动是否存在重叠、研究中的病例数量以及研究所在的国家。我们最好的多元模型确定了培训、使用双视图乳腺 X 线摄影和 BI-RADS 类别（肿块、钙化和最终评估）是κ的预测因素。培训、使用双视图乳腺 X 线摄影和关注肿块描述可能有助于提高乳腺 X 线摄影解读的可靠性。钙化和最终评估描述符是潜在改进的领域。这些发现对于在不同环境中引入该系统之前在 BI-RADS 使用方面实施政策以及改进当前实施非常重要。
