Model Agreement in Cancer Diagnostic Tests

癌症诊断测试中的模型协议

• 批准号: 8629037
• 负责人: KERRIE P NELSON
• 金额: $ 26.06万
• 依托单位: BOSTON UNIVERSITY MEDICAL CAMPUS
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8629037
• 关键词: 3-Dimensional; Accounting; Address; Affect; Agreement; American College of Radiology; Behavior; Breast; Breast Cancer Detection; Breast Cancer Surveillance Consortium; Cancer Diagnostics; Characteristics; Classification; Clinical; Communities; Community Developments; Computer software; Data; Data Analyses; Development; Diagnosis; Diagnostic; Diagnostic Procedure; Diagnostic tests; Digital Mammography; Effectiveness; Elements; Future; Goals; Heterogeneity; Image; Individual; Influentials; Lead; Malignant Neoplasms; Mammography; Measures; Medical; Methods; Modeling; National Cancer Institute; Patients; Performance; Play; Procedures; Property; Public Health; Reading; Recording of previous events; Role; Statistical Methods; Technology; Test Result; Testing; Training; Translating; breast density; experience; flexibility; improved; insight; malignant breast neoplasm; novel; novel diagnostics; population based; public health relevance; radiologist; screening; tool

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.

7. 项目总结/摘要 许多癌症诊断测试涉及医学专家使用有序的方法对患者进行分类 分类尺度。此类测试涉及专家的主观性和估计因素，因为 解释不完美的诊断测试结果的必要性，导致专家之间的差异 分类通常很严重，即使是在常见的诊断程序中，例如乳房X线照相术和 乳腺密度的分类，乳腺癌的重要预测因素。这激发了许多大型 将进行研究以检验专家之间在共同诊断环境中的一致程度，并 调查评级者经验等因素是否影响不同专家评级的一致性。 然而，目前用于评估此类大规模研究一致性的统计方法有限。 我们的总体目标有两个：（1）开发新颖且灵活的统计方法和协议措施 用于在使用一名或多名医学专家参与的大规模研究中评估可靠性 具有有序分类量表的诊断测试，以及（2）使用这些方法来评估最近的可靠性 进行了大规模乳腺癌和乳腺密度研究，并检查以下因素的影响 评估者的经验和患者的既往病史可以在这些人群的可靠性中发挥重要作用- 基础设置。由于筛查性乳房X光检查在社区中的广泛使用，得出的结论 我们对诊断测试中大规模一致性研究的分析将具有重大而深远的影响 对社区乳腺癌筛查和诊断的影响。我们提出的方法 应用程序提供了一种新颖且全面的方法来检查大规模研究中的一致性 当专家根据以下标准对主题进行分类时，重点评估和比较专家之间的一致性 诊断测试中的有序分类分类量表。开发的方法将免费提供 并使用标准统计软件轻松实施。我们对大规模癌症协议的分析 使用我们提出的方法进行的研究将为筛选解释性能提供新的见解 放射科医生。