目的:评估英国筛查队列中乳腺密度和年龄类别的乳腺癌筛查性能。
方法:来自英国单个站点的原始全场数字乳房X线摄影数据,回顾性获得了2016年至2018年连续3年的50~70岁女性队列.使用Volpara软件评估乳腺密度。检查按密度类别和年龄组(50-60岁和61-70岁)分组,以分析筛查性能。进行统计分析以确定密度类别和年龄组之间的关联。将乳腺体积密度评估为间隔癌(IC)的二元分类器以找到最佳密度阈值。
结果:四万九千九百四十八项筛查检查(409项筛查癌症(SDC)和205项IC)被纳入分析。乳腺摄影灵敏度,SDC/(SDC+IC),随着乳腺密度从密度a的75.0%增加而下降(p=0.839,与b类比较),73.5%,59.8%(p=0.001),b类占51.3%(p<0.001),C,d,分别。IC率在密度最高的类别中最高,为1.8(p=0.039),3.2、5.7(p<0.001),a类为每千人7.9(p<0.001),B,C,d,分别。召回率随着乳腺密度的增加而增加,导致更多的假阳性召回,尤其是在年轻的年龄组。发现的最佳密度阈值之间没有显着差异,6.85,而Volpara定义为b/c边界,7.5.
结论:筛查的性能随着密度的增加而显著降低,在最密集的类别中,IC率比具有脂肪乳房的女性高4倍。对于没有事先检查的年轻亚组,假阳性是一个特殊的问题。
结论:在接受筛查的女性中,在乳房致密的女性中,乳腺癌的诊断明显不足,在密度最高的类别中最明显,但仍比第二高类别中乳房脂肪的女性高三倍。
结论:乳腺密度可以掩盖癌症,导致乳房X线摄影诊断不足。间隔性癌症发生率随着乳腺密度类别\'a\'至\'d\'而增加;1.8至7.9/千分之。召回率随着乳腺密度的增加而增加,导致更多的假阳性召回。
OBJECTIVE: To assess the performance of breast cancer screening by category of breast density and age in a UK screening cohort.
METHODS: Raw full-field digital mammography data from a single site in the UK, forming a consecutive 3-year cohort of women aged 50 to 70 years from 2016 to 2018, were obtained retrospectively. Breast density was assessed using Volpara software. Examinations were grouped by density category and age group (50-60 and 61-70 years) to analyse screening performance. Statistical analysis was performed to determine the association between density categories and age groups. Volumetric breast density was assessed as a binary classifier of interval cancers (ICs) to find an optimal density threshold.
RESULTS: Forty-nine thousand nine-hundred forty-eight screening examinations (409 screen-detected cancers (SDCs) and 205 ICs) were included in the analysis. Mammographic sensitivity, SDC/(SDC + IC), decreased with increasing breast density from 75.0% for density a (p = 0.839, comparisons made to category b), to 73.5%, 59.8% (p = 0.001), and 51.3% (p < 0.001) in categories b, c, and d, respectively. IC rates were highest in the densest categories with rates of 1.8 (p = 0.039), 3.2, 5.7 (p < 0.001), and 7.9 (p < 0.001) per thousand for categories a, b, c, and d, respectively. The recall rate increased with breast density, leading to more false positive recalls, especially in the younger age group. There was no significant difference between the optimal density threshold found, 6.85, and that Volpara defined as the b/c boundary, 7.5.
CONCLUSIONS: The performance of screening is significantly reduced with increasing density with IC rates in the densest category four times higher than in women with fatty breasts. False positives are a particular issue for the younger subgroup without prior examinations.
CONCLUSIONS: In women attending screening there is significant underdiagnosis of breast cancer in those with dense breasts, most marked in the highest density category but still three times higher than in women with fatty breasts in the second highest category.
CONCLUSIONS: Breast density can mask cancers leading to underdiagnosis on mammography. Interval cancer rate increased with breast density categories \'a\' to \'d\'; 1.8 to 7.9 per thousand. Recall rates increased with increasing breast density, leading to more false positive recalls.