Wanders Johanna O P, Holland Katharina, Veldhuis Wouter B, Mann Ritse M, Pijnappel Ruud M, Peeters Petra H M, van Gils Carla H, Karssemeijer Nico
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, P.O. Box 85500, 3508 GA, Utrecht, The Netherlands.
Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands.
Breast Cancer Res Treat. 2017 Feb;162(1):95-103. doi: 10.1007/s10549-016-4090-7. Epub 2016 Dec 23.
To determine to what extent automatically measured volumetric mammographic density influences screening performance when using digital mammography (DM).
We collected a consecutive series of 111,898 DM examinations (2003-2011) from one screening unit of the Dutch biennial screening program (age 50-75 years). Volumetric mammographic density was automatically assessed using Volpara. We determined screening performance measures for four density categories comparable to the American College of Radiology (ACR) breast density categories.
Of all the examinations, 21.6% were categorized as density category 1 ('almost entirely fatty') and 41.5, 28.9, and 8.0% as category 2-4 ('extremely dense'), respectively. We identified 667 screen-detected and 234 interval cancers. Interval cancer rates were 0.7, 1.9, 2.9, and 4.4‰ and false positive rates were 11.2, 15.1, 18.2, and 23.8‰ for categories 1-4, respectively (both p-trend < 0.001). The screening sensitivity, calculated as the proportion of screen-detected among the total of screen-detected and interval tumors, was lower in higher density categories: 85.7, 77.6, 69.5, and 61.0% for categories 1-4, respectively (p-trend < 0.001).
Volumetric mammographic density, automatically measured on digital mammograms, impacts screening performance measures along the same patterns as established with ACR breast density categories. Since measuring breast density fully automatically has much higher reproducibility than visual assessment, this automatic method could help with implementing density-based supplemental screening.
确定在使用数字乳腺摄影(DM)时,自动测量的乳腺体积密度对筛查性能的影响程度。
我们从荷兰两年一次的筛查项目(年龄50 - 75岁)的一个筛查单位收集了连续的111,898例DM检查(2003 - 2011年)。使用Volpara自动评估乳腺体积密度。我们确定了与美国放射学会(ACR)乳腺密度类别相当的四个密度类别的筛查性能指标。
在所有检查中,21.6%被归类为密度类别1(“几乎全是脂肪”),41.5%、28.9%和8.0%分别被归类为类别2 - 4(“极度致密”)。我们识别出667例筛查发现的癌症和234例间期癌。1 - 4类别的间期癌发生率分别为0.7‰、1.9‰、2.9‰和4.4‰,假阳性率分别为11.2‰、15.1‰、18.2‰和23.8‰(两者p趋势<0.001)。以筛查发现的肿瘤在筛查发现的肿瘤和间期肿瘤总数中所占比例计算的筛查敏感性,在较高密度类别中较低:1 - 4类别的筛查敏感性分别为85.7%、77.6%、69.5%和61.0%(p趋势<0.001)。
在数字乳腺摄影上自动测量的乳腺体积密度,对筛查性能指标的影响模式与ACR乳腺密度类别所确定的模式相同。由于完全自动测量乳腺密度比视觉评估具有更高的可重复性,这种自动方法有助于实施基于密度的补充筛查。