Improved DBM ROC Methods for Diagnostic Radiology

描述(由申请人提供)： 这项建议解决了诊断放射学研究中的一个基本方法学问题：如何评估成像方式的诊断性能。受试者工作特性(ROC)分析是一种公认的程序，用于测量成像设备允许医生检测疾病的程度。传统的不同成像模式的统计比较只分析每种模式下单个放射科医生的ROC测量。Dorfman，Berbaum和Metz(1992)发展了一种统计方法，克服了这一局限，并提供了一个全面的框架，在此框架内分析有多个读者的ROC研究，同时考虑到读者样本和案例样本的差异。与单一放射科医生的方法相比，多夫曼-伯鲍姆-梅茨(DBM)方法允许更大的统计能力来检测诊断性能中有临床意义的差异。它的有效性已经用计算机模拟进行了广泛的测试，并在放射学诊断实验中得到了广泛的接受。由于DBM方法的相依测量依赖于ROC分析，因此ROC分析的稳健性和准确性影响DBM方法的统计精度和能力。由于传统ROC度量的缺陷，一些实验的DBM分析失败。自DBM方法发展以来，ROC分析取得了根本性的进展，提供了更多可解释的衡量标准。如果DBM利用这些进步，它可能会更加健壮，在统计上也更强大。为了实现这一点，我们建议建立一个集成的、公共领域的软件资源，将以前只有在单独的程序中才能获得的方法优势结合在一起。具体目标是：(1)开发一个新的模块化的DBM软件体系结构，可由其他软件自由访问，并允许在ROC分析中有更大的灵活性；(2)开发“适当的”ROC分析模块，提供更可解释的ROC结果，并研究由此产生的DBC统计能力；(3)开发针对ROC分析类型的模块，该模块考虑到报告疾病的位置和多个疾病报告，这些特征已被证明可提高统计能力；(4)开发模块和程序，将DBM方法与标准统计软件相结合，以回答更复杂的实验问题；以及(5)使用DBM对公布数据的重新分析，以提供检测不同成像区域的具有功率的各种性能差异所需的最小读取器和外壳样本大小组合的估计。完成这些目标将提高用于评估新成像系统诊断性能的主要方法的有效性。这将导致更好的放射学研究，这将提高医疗保健的质量和降低成本。

DESCRIPTION (provided by applicant): This proposal addresses a fundamental methodological problem in diagnostic radiology research: how to evaluate the diagnostic performance of imaging modalities. Receiver operating characteristic (ROC) analysis is an accepted procedure for measuring how well imaging modalities allow physicians to detect disease. Traditional statistical comparison of different imaging modalities only analyzes ROC measures of a single radiologist from each modality. Dorfman, Berbaum and Metz (1992) developed a statistical methodology that overcomes this limitation and provides a comprehensive framework within which to analyze ROC studies with multiple readers, taking into account both reader-sample and case-sample variation. The Dorfman-Berbaum-Metz (DBM) methodology allows greater statistical power to detect clinically meaningful differences in diagnostic performance than single-radiologist approaches. Its validity has been tested extensively using computer simulations and has wide acceptance in diagnostic radiology experiments. Because DBM methodology relies on ROC analysis for its dependent measures, the robustness and accuracy of the ROC analysis affect the statistical precision and power of DBM methodology. DBM analysis of some experiments fails because of flaws in traditional ROC measures. Since the DBM methodology was developed, there have been fundamental advances in ROC analysis, which provide more interpretable measures. DBM could be more robust and statistically powerful if it took advantage of these advances. To accomplish this we propose building an integrated, public-domain software resource to bring together methodological advantages previously only available in separate programs. The specific aims are: (1) develop a new modular DBM software architecture that is freely accessible by other software and allows greater flexibility in ROC analysis; (2) develop "proper" ROC analysis modules that provide more interpretable ROC results and study the resulting DBM statistical power; (3) develop modules for types of ROC analysis that take into account location of reported disease and multiple reports of disease, features that have been shown to improve statistical power; (4) develop modules and procedures to merge DBM methodology with standard statistical software to answer more complex experimental questions; and (5) use DBM re-analysis of published data to provide estimates of minimum reader and case sample size combinations needed to detect various performance differences with power for various imaging areas. Completing these aims will enhance the effectiveness of the primary method used to assess the diagnostic performance of new imaging systems. This will lead to better radiology research, which will raise the quality and lower the cost of health care.