Improving Accuracy and Reliability in Cancer Screening Tests

项目摘要 目前筛查测试的临床实践涉及对患者测试结果的主观解释，例如 由训练有素的专家进行乳房X光检查。放射科医生的视力之间经常报告有很大的差异 影响常见筛查试验准确性和一致性的乳房图像分类 包括乳房X光检查。与患者和评分者相关的因素以及技术本身可能会影响 专家对乳腺癌和密度的评级，这是乳腺癌的重要预测指标。然而，对其的研究 在大规模癌症纵向筛查研究中放射科医生评分的准确性和一致性 由于分类的顺序性和许多专家各自贡献评级，这是具有挑战性的。 包括自动化3-D程序在内的新出现的过程为评估提供了令人兴奋的潜力 常规临床环境中的乳房密度。目前很少有统计方法和汇总措施 存在对几个放射科医生的顺序评级之间的一致性和准确性的建模。此外，几乎没有人 方法可以调查患者和放射科医生的特点，使用自动化 程序和不同技术在准确性和一致性上的比较。 我们的目标是开发基于广义线性混合模型和潜在的新的统计方法 在大规模筛查研究中，在许多专家之间研究准确性和一致性的可变模型。 我们的方法可以灵活地容纳许多专家的评分和其他因素来检查他们的影响 关于一致性和准确性。我们将推出新的基于模型的协议摘要衡量标准，并 精确度。我们将在最近的大规模乳房成像研究中应用我们的新统计方法。一把钥匙 我们建议的研究的优势是为医学研究人员提供灵活的建模方法和 同时利用所有数据的新的汇总措施，其中可以得出以下结论 人群中典型专家和患者之间的一致性，极大地提高了效率和 权力。患者和评分者特征在一致性和准确性水平上的研究 评分员的分类将转化为在培训放射科医生和口译实践方面的改进 乳房X光检查，最终是一种更有效的乳房筛查程序。

Project Summary Current clinical practice in screening tests involves subjective interpretation of patients' test results such as mammograms by trained experts. Substantial variability is often reported between radiologists' visual classifications of breast images, impacting the accuracy and consistency of common screening tests including mammography. Factors related to patients and raters and the technology itself may impact experts' ratings of breast cancer and density, an important predictor of breast cancer. However, the study of accuracy and consistency between radiologists' ratings in large-scale cancer longitudinal screening studies is challenging due to the ordinal nature of the classifications and many experts each contributing ratings. Newly emerging processes including automated 3-D procedures provide exciting potential for estimating breast density in routine clinical settings. Currently very few statistical approaches and summary measures exist to model the consistency and accuracy between several radiologists' ordinal ratings. Further, few methods can investigate the influence of patient and radiologist characteristics, the use of automated procedures and comparison of the different technologies upon accuracy and consistency. Our goals are to develop new statistical methods based upon generalized linear mixed models and latent variable models to study accuracy and consistency amongst many experts in large-scale screening studies. Our approach can flexibly accommodate many experts' ratings and other factors to examine their influence on consistency and accuracy. We will derive novel model-based summary measures of agreement and accuracy. We will implement our new statistical methods in recent large-scale breast imaging studies. A key strength of our proposed research is to provide medical researchers with a flexible modeling approach and novel summary measures that utilize all the data simultaneously, where conclusions can be drawn about the consistency between typical experts and patients in the populations, greatly increasing efficiency and power. The study of patient and rater characteristics on the levels of consistency and accuracy between raters' classifications will translate to improvements in training radiologists and practice of interpreting mammograms, and ultimately, a more effective breast screening procedure.