Evaluating the effectiveness of using standard mammogram form to predict breast cancer risk: Case-control study
Evaluating the effectiveness of using standard mammogram form to predict breast cancer risk: Case-control study
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DOI：
10.1158/1055-9965.epi-07-2634
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发表时间：
2008-05-01
影响因子：
3.8
通讯作者：
Easton, Douglas
中科院分区：
文献类型：
--
作者：
Ding, Jane;Warren, Ruth;Easton, Douglas
乳腺密度是一个众所周知的乳腺癌风险因素。目前大多数测量乳腺密度的方法都是基于面积和主观的。标准乳房X线照片表格（SMF）是一种计算机程序，使用体积方法来估计乳房中的密度百分比。本研究的目的是通过与其他广泛使用的密度测量方法进行比较，评估SMF作为乳腺癌风险预测因子的当前实施情况。该病例对照研究包括634例癌症患者和1,880例年龄匹配的对照，这些对照来自剑桥和诺维奇乳腺筛查项目。数据收集涉及评估电影的基础上沃尔夫的实质模式和视觉估计的百分比密度，然后数字化的电影计算机分析（交互式阈值技术和SMF）。Logistic回归用于产生与乳腺密度增加类别相关的比值比。所有四种方法的密度测量与总体人群中的乳腺癌风险密切相关。通过阈值法测量的与密度增加相关的风险逐步增加为1.37 [95%置信区间（95% CI），1.03-1.821，1.80（95% CI，1.36-2.37）和2.45（95% CI，1.86-3.23）。对于SMF密度测量值的每个递增四分位数，风险分别为1.11（95% CI，0.85-1.46）、1.31（95% CI，1.001.71）和1.92（95% CI，1.47-2.51）。在模型针对SMF结果进行调整后，阈值读数在密度-风险关系中保持相同的强烈逐步增加。相反，一旦模型针对阈值读数进行调整，SMF结果就不再与癌症风险相关。与阈值方法相比，SMF的可用实现不是更好的癌症风险预测器。
Breast density is a well-known breast cancer risk factor. Most current methods of measuring breast density are area based and subjective. Standard mammogram form (SMF) is a computer program using a volumetric approach to estimate the percent density in the breast. The aim of this study is to evaluate the current implementation of SMF as a predictor of breast cancer risk by comparing it with other widely used density measurement methods. The case-control study comprised 634 cancers with 1,880 age-matched controls combined from the Cambridge and Norwich Breast Screening Programs. Data collection involved assessing the films based both on Wolfe's parenchymal patterns and on visual estimation of percent density and then digitizing the films for computer analysis (interactive threshold technique and SMF). Logistic regression was used to produce odds ratios associated with increasing categories of breast density. Density measures from all four methods were strongly associated with breast cancer risk in the overall population. The stepwise rises in risk associated with increasing density as measured by the threshold method were 1.37 [95% confidence interval (95% CI), 1.03-1.821, 1.80 (95% CI, 1.36-2.37), and 2.45 (95% CI, 1.86-3.23). For each increasing quartile of SMF density measures, the risks were 1.11 (95% CI, 0.85-1.46), 1.31 (95% CI, 1.001.71), and 1.92 (95% CI, 1.47-2.51). After the model was adjusted for SMF results, the threshold readings maintained the same strong stepwise increase in density-risk relationship. On the contrary, once the model was adjusted for threshold readings, SMF outcome was no longer related to cancer risk. The available implementation of SMF is not a better cancer risk predictor compared with the thresholding method.