Department of Diagnostic Radiology, Dalhousie University, Halifax, NS, Canada B3H 2Y9.
Comput Math Methods Med. 2013;2013:651091. doi: 10.1155/2013/651091. Epub 2013 May 8.
Visual assessments of mammographic breast density by radiologists are used in clinical practice; however, these assessments have shown weaker associations with breast cancer risk than area-based, quantitative methods. The purpose of this study is to present a statistical evaluation of a fully automated, area-based mammographic density measurement algorithm. Five radiologists estimated density in 5% increments for 138 "For Presentation" single MLO views; the median of the radiologists' estimates was used as the reference standard. Agreement amongst radiologists was excellent, ICC = 0.884, 95% CI (0.854, 0.910). Similarly, the agreement between the algorithm and the reference standard was excellent, ICC = 0.862, falling within the 95% CI of the radiologists' estimates. The Bland-Altman plot showed that the reference standard was slightly positively biased (+1.86%) compared to the algorithm-generated densities. A scatter plot showed that the algorithm moderately overestimated low densities and underestimated high densities. A box plot showed that 95% of the algorithm-generated assessments fell within one BI-RADS category of the reference standard. This study demonstrates the effective use of several statistical techniques that collectively produce a comprehensive evaluation of the algorithm and its potential to provide mammographic density measures that can be used to inform clinical practice.
放射科医生对乳腺钼靶密度进行视觉评估在临床实践中得到应用；然而，这些评估与乳腺癌风险的相关性比基于面积的定量方法要弱。本研究旨在对一种全自动、基于面积的乳腺密度测量算法进行统计学评估。五位放射科医生对 138 例“供展示”的单侧 MLO 视图以 5%的增量进行密度估计；将放射科医生的中位数估计值作为参考标准。放射科医生之间的一致性非常好，ICC=0.884，95%CI（0.854，0.910）。同样，算法与参考标准之间的一致性也非常好，ICC=0.862，在放射科医生估计值的 95%CI 范围内。Bland-Altman 图显示参考标准与算法生成的密度相比略有正偏倚（+1.86%）。散点图显示，该算法中度高估了低密度，低估了高密度。箱线图显示，算法生成的评估结果有 95%落在参考标准的一个 BI-RADS 类别内。本研究展示了几种统计技术的有效应用，这些技术共同对算法进行了全面评估，并展示了其提供可用于指导临床实践的乳腺密度测量值的潜力。
