超越乳腺影像报告和数据系统（BI-RADS）密度：呼吁乳腺影像诊所进行量化
Beyond BI-RADS Density: A Call for Quantification in the Breast Imaging Clinic.
作者信息
Conant Emily F, Sprague Brian L, Kontos Despina
机构信息
From the Department of Radiology, Hospital of the University of Pennsylvania, Perelman School of Medicine, 3400 Spruce St, Philadelphia PA 10104 (E.F.C., D.K.); and Departments of Surgery and Radiology, University of Vermont Cancer Center, Burlington, Vt (B.L.S.).
出版信息
Radiology. 2018 Feb;286(2):401-404. doi: 10.1148/radiol.2017170644.
Abstract
Ultimately, the incorporation of automated quantitative measures of breast density will lead to more effective clinical care and more robust outcomes research than the current, subjective assignment of Breast Imaging and Reporting Data System density categories, by providing reproducible estimates of both the risk of masking a cancer as well as the risk of developing breast cancer—two important factors in determining personalized breast cancer screening algorithms.
摘要
最终，与目前主观指定的乳腺影像报告和数据系统密度类别相比，纳入乳腺密度的自动定量测量将带来更有效的临床护理和更可靠的结果研究，因为它能提供关于掩盖癌症风险以及患乳腺癌风险的可重复估计，而这两个因素是确定个性化乳腺癌筛查算法的重要因素。
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