ROC Analysis of Imaging System Evaluations

超出所提供的空间。在评估成像系统是否可能用于临床环境以及监管批准过程中，技术和实践的诊断性能评估是决策过程中越来越重要的一部分。在医学成像中，这些评估的很大一部分包括使用ROC类型响应或其衍生物进行分析的任务特定评级。这个项目的总体目标是提高我们的实际（方法）和计算（分析相关）的问题，我们遇到的系统评估和比较时，观察员，计算机化的计划（如CAD），或两者的组合成为诊断系统的一个组成部分，我们的理解。在当前的资助期间，我们开发并测试了一种新的方法来分析ROC类型研究期间确定的评分数据，并开始解决与将基于相关性的实用程序纳入分析相关的问题。基本方法使用阴性和阳性病例（检查）之间的所有配对观察结果，使用非参数方法构建性能的汇总指数。无条件和有条件的测试已经开发和测试，并显示出具有明显的实验优势，小样本量的研究。五个主要的努力，建议使用这种一般方法。我们建议将该方法扩展到多读者，多模态，多疾病设置，以及将各种效用函数的分析。最后，我们将研究如何使用这种方法和其他方法来识别和处理ROC类型研究中的离群值。性能现场=

EXCEED THE SPACE PROVIDED. Assessment of diagnostic performance of technologies and practices constitute an increasingly important part of the decision making process when imaging systems are evaluated for possible use in the clinical environment as well as during the regulatory approval process. In medical imaging a substantial fraction of these evaluations include task specific ratings that are being analyzed using an ROC type response or derivative thereof. The overall objective of this project is to improve our understanding of the practical (methodological) and computational (analyses related) issues that we encounter during systems evaluations and comparisons when the observer, a computerized scheme (e.g. CAD), or a combination of both become an integral part of the diagnostic system. During the current period of funding we developed and tested a new approach to analyzing scoring data ascertained during the performance of ROC type studies and began to address issues related to the incorporation of relevance based utilities into the analyses. The underlying approach uses all paired observations between negative and positive cases (examinations) to construct summary indices of performance using a non-parametric methodology. Both unconditional and conditional tests have been developed and tested and were shown to have distinct experimental advantages for small sample size studies. Five primary efforts are proposed using this general approach. We propose to expand the method to a multi-reader, multi-modality, multi-disease settings as well as to incorporate a variety of utility functions into the analysis. Last, we will investigate how this and other methods can be used to identify and possibly treat outliers in ROC type studies. PERFORMANCE SITE ========================================Section End===========================================