Suppr超能文献

用于比较成像系统效能的ROC和自由反应范式的临床相关性。

Clinical relevance of the ROC and free-response paradigms for comparing imaging system efficacies.

作者信息

Chakraborty D P

机构信息

Department of Radiology, University of Pittsburgh, Pittsburgh, PA, USA.

出版信息

Radiat Prot Dosimetry. 2010 Apr-May;139(1-3):37-41. doi: 10.1093/rpd/ncq017. Epub 2010 Feb 5.

Abstract

Observer performance studies are widely used to assess medical imaging systems. Unlike technical/engineering measurements observer performance include the entire imaging chain and the radiologist. However, the widely used receiver operating characteristic (ROC) method ignores lesion localisation information. The free-response ROC (FROC) method uses the location information to appropriately reward or penalise correct or incorrect localisations, respectively. This paper describes a method for improving the clinical relevance of FROC studies. The method consists of assigning appropriate risk values to the different lesions that may be present on a single image. A high-risk lesion is one that is critical to detect and act upon, and is assigned a higher risk value than a low-risk lesion, one that is relatively innocuous. Instead of simply counting the number of lesions that are detected, as is done in conventional FROC analysis, a risk-weighted count is used. This has the advantage of rewarding detections of high-risk lesions commensurately more than detections of lower risk lesions. Simulations were used to demonstrate that the new method, termed case-based analysis, results in a higher figure of merit for an expert who detects more high-risk lesions than a naive observer who detects more low-risk lesions, even though both detect the same total number of lesions. Conventional free-response analysis is unable to distinguish between the two types of observers. This paper also comments on the issue of clinical relevance of ROC analysis vs. FROC for tasks that involve lesion localisation.

摘要

观察者性能研究被广泛用于评估医学成像系统。与技术/工程测量不同,观察者性能涵盖了整个成像链以及放射科医生。然而,广泛使用的接收器操作特性(ROC)方法忽略了病变定位信息。自由响应ROC(FROC)方法利用位置信息分别对正确或错误的定位给予适当的奖励或惩罚。本文描述了一种提高FROC研究临床相关性的方法。该方法包括为单个图像上可能出现的不同病变赋予适当的风险值。高风险病变是指检测和处理起来至关重要的病变,其被赋予的风险值高于低风险病变,低风险病变相对无害。与传统FROC分析中简单计算检测到的病变数量不同,这里使用的是风险加权计数。这样做的优点是,相比于低风险病变的检测,对高风险病变的检测给予了相应更多的奖励。通过模拟证明,这种被称为基于病例分析的新方法,对于检测出更多高风险病变的专家而言,其优值高于检测出更多低风险病变的新手观察者,即便两者检测到的病变总数相同。传统的自由响应分析无法区分这两种类型的观察者。本文还对涉及病变定位任务时ROC分析与FROC的临床相关性问题进行了评论。

相似文献

2
Consistency of methods for analysing location-specific data.特定位置数据分析方法的一致性。
Radiat Prot Dosimetry. 2010 Apr-May;139(1-3):52-6. doi: 10.1093/rpd/ncq030. Epub 2010 Feb 16.
3
A status report on free-response analysis.自由反应分析的状态报告。
Radiat Prot Dosimetry. 2010 Apr-May;139(1-3):20-5. doi: 10.1093/rpd/ncp305. Epub 2010 Jan 18.
6
Ideal AFROC and FROC observers.理想的 AFROC 和 FROC 观察者。
IEEE Trans Med Imaging. 2010 Feb;29(2):375-86. doi: 10.1109/TMI.2009.2031840.
8
ROC, LROC, FROC, AFROC: an alphabet soup.ROC、LROC、FROC、AFROC:一堆字母组合。
J Am Coll Radiol. 2009 Sep;6(9):652-5. doi: 10.1016/j.jacr.2009.06.001.

本文引用的文献

10
ROC curves predicted by a model of visual search.由视觉搜索模型预测的ROC曲线。
Phys Med Biol. 2006 Jul 21;51(14):3463-82. doi: 10.1088/0031-9155/51/14/013. Epub 2006 Jul 6.

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验