Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA.
Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, USA.
Cancer Causes Control. 2019 Oct;30(10):1145-1155. doi: 10.1007/s10552-019-01208-9. Epub 2019 Aug 3.
The American Cancer Society (ACS) suggests using a stratified strategy for breast cancer screening. The strategy includes assessing risk of breast cancer, screening women at high risk with both MRI and mammography, and screening women at low risk with mammography alone. The ACS chose their cutoff for high risk using expert consensus.
We propose instead an analytic approach that maximizes the diagnostic accuracy (AUC/ROC) of a risk-based stratified screening strategy in a population. The inputs are the joint distribution of screening test scores, and the odds of disease, for the given risk score. Using the approach for breast cancer screening, we estimated the optimal risk cutoff for two different risk models: the Breast Cancer Screening Consortium (BCSC) model and a hypothetical model with much better discriminatory accuracy. Data on mammography and MRI test score distributions were drawn from the Magnetic Resonance Imaging Screening Study Group.
A risk model with an excellent discriminatory accuracy (c-statistic [Formula: see text]) yielded a reasonable cutoff where only about 20% of women had dual screening. However, the BCSC risk model (c-statistic [Formula: see text]) lacked the discriminatory accuracy to differentiate between women who needed dual screening, and women who needed only mammography.
Our research provides a general approach to optimize the diagnostic accuracy of a stratified screening strategy in a population, and to assess whether risk models are sufficiently accurate to guide stratified screening. For breast cancer, most risk models lack enough discriminatory accuracy to make stratified screening a reasonable recommendation.
美国癌症协会（ACS）建议采用分层策略进行乳腺癌筛查。该策略包括评估乳腺癌风险，对高风险女性同时进行 MRI 和乳房 X 光检查筛查，对低风险女性仅进行乳房 X 光检查筛查。ACS 选择使用专家共识来确定高风险的截止值。
我们提出了一种分析方法，该方法可最大限度地提高基于风险的分层筛查策略在人群中的诊断准确性（AUC/ROC）。输入是给定风险评分的筛查测试分数和疾病几率的联合分布。我们使用乳腺癌筛查的方法，估计了两种不同风险模型的最佳风险截止值：乳腺癌筛查联盟（BCSC）模型和具有更好区分准确性的假设模型。关于乳房 X 光和 MRI 测试分数分布的数据来自磁共振成像筛查研究小组。
具有出色区分准确性（c 统计量 [公式：见文本]）的风险模型产生了一个合理的截止值，只有约 20%的女性需要双重筛查。然而，BCSC 风险模型（c 统计量 [公式：见文本]）缺乏足够的区分准确性，无法区分需要双重筛查的女性和仅需要乳房 X 光检查的女性。
我们的研究提供了一种优化人群中分层筛查策略的诊断准确性的一般方法，并评估了风险模型是否足够准确以指导分层筛查。对于乳腺癌，大多数风险模型缺乏足够的区分准确性，无法使分层筛查成为合理的建议。
