Model Agreement in Cancer Diagnostic Tests

7.项目摘要/摘要 许多癌症诊断测试涉及由医学专家使用已订购的 绝对标尺。此类测试涉及专家部分的主观性和评估因素，原因是 有必要解释不完美的诊断测试结果，导致专家之间的差异 分类，往往是严重的，即使在常见的诊断程序，如乳房X光照相和在 乳房密度分类是乳腺癌的重要预测指标。这促使了许多大规模的 将进行研究，以检查共同诊断环境中专家之间的一致程度，并 调查评价员经验等因素是否会影响不同专家做出的评分的一致性。 然而，目前在这类大规模研究中评估一致性的统计方法有限。 我们的总体目标有两个：(1)发展新的和灵活的统计方法和一致性措施 用于评估涉及两个或多个医学专家的大规模研究中使用一个或多个 (2)使用这些方法来评估最近的可靠性 进行了大规模的乳腺癌和乳房密度研究，并检查了以下因素的影响 评分员的经验和患者的既往病史在这些人群的可靠性中起着重要作用- 基于设置。由于筛查乳房X光检查在社区中的广泛使用，从 我们对诊断测试中的大规模一致性研究的分析将具有重要而深远的意义 对社区乳腺癌筛查和诊断的影响。在我们的 应用程序提供了一种新的、全面的方法来检查大规模研究和 重点评估和比较专家对主题进行分类时的一致性 诊断试验中的有序分类标尺。开发的方法将免费提供 并使用标准统计软件轻松实现。我们对大规模癌症协议的分析 使用我们提出的方法的研究将为筛选解释性能提供新的见解 放射科医生。

7. Project Summary/Abstract Many cancer diagnostic tests involve the classification of a patient by a medical expert using an ordered categorical scale. Such tests involve elements of subjectivity and estimation on the part of the expert due to the necessity to interpret imperfect diagnostic test results, leading to discrepancies between experts' classifications, often severely so, even in common diagnostic procedures such as mammography and in the classification of breast density, an important predictor of breast cancer. This has motivated many large-scale studies to be conducted to examine levels of agreement between experts in common diagnostic settings and to investigate if factors such as rater experience affect the consistency of ratings made by different experts. However, limited statistical methods currently exist to assess agreement in large-scale studies such as these. Our overall goals are two-fold: (1) to develop novel and flexible statistical methods and agreement measures for assessing reliability in large-scale studies involving two or more medical experts when using one or more diagnostic tests with ordered categorical scales, and (2) to use these methods to assess reliability in recently conducted large-scale breast cancer and breast density studies and to examine the impact of factors such as rater experience and the patient's prior history that can play important roles in reliability in these population- based settings. Due to widespread use of screening mammography in the community, conclusions drawn from our analyses of large-scale agreement studies in diagnostic testing will have significant and far-reaching implications for breast cancer screening and diagnosis in the community. The proposed methods in our application provide a novel and comprehensive approach to examine agreement in large-scale studies and focus on assessing and comparing agreement between experts when they classify subjects according to ordered categorical classification scales in diagnostic tests. Methods developed will be made freely available and easily implemented using standard statistical software. Our analyses of large-scale cancer agreement studies using our proposed methods will provide new insights into the screening interpretative performance of radiologists.