Assessment methodologies and statistical issues for computer-aided diagnosis of lung nodules in computed tomography: Contemporary research topics relevant to the lung image database consortium
Assessment methodologies and statistical issues for computer-aided diagnosis of lung nodules in computed tomography: Contemporary research topics relevant to the lung image database consortium
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DOI：
10.1016/s1076-6332(03)00814-6
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发表时间：
2004-04-01
影响因子：
4.8
通讯作者：
Sayre, J
中科院分区：
文献类型：
--
作者：
Dodd, LE;Wagner, RF;Sayre, J
肺癌和支气管癌是美国最主要的致命恶性肿瘤。5年生存率很低，但对早期疾病的治疗大大提高了生存率。多检测器行计算机断层扫描技术的进步提供了小肺结节的检测，并提供了一种潜在有效的筛查工具。然而，每次检查的大量图像需要相当多的放射科医生时间来解释，并且是临床吞吐量的障碍。因此，需要计算机辅助诊断（CAD）方法来协助放射科医生做出决策。为了促进CAD方法的发展，美国国家癌症研究所成立了肺图像数据库联盟（LIDC）。LIDC负责制定共识和标准，以创建多检测器行计算机断层扫描肺部图像数据库，作为CAD研究人员的资源。为了开发这样一个前瞻性的数据库，必须预测它的潜在用途。最终的应用程序将影响必须与图像一起包含的信息、算法性能的相关度量以及所需图像的数量。在本文中，我们概述了与LIDC数据库的几种潜在用途相关的评估方法和统计问题。我们回顾了绩效评估的方法，并讨论了定义“真相”的问题，以及当真相信息不可用时出现的并发症。我们还讨论了有关调整数据库大小和填充数据库的问题。(c) aur， 2004年。
Cancer of the lung and bronchus is the leading fatal malignancy in the United States. Five-year survival is low, but treatment of early stage disease considerably improves chances of survival. Advances in multidetector-row computed tomography technology provide detection of smaller lung nodules and offer a potentially effective screening tool. The large number of images per exam, however, requires considerable radiologist time for interpretation and is an impediment to clinical throughput. Thus, computer-aided diagnosis (CAD) methods are needed to assist radiologists with their decision making. To promote the development of CAD methods, the National Cancer Institute formed the Lung Image Database Consortium (LIDC). The LIDC is charged with developing the consensus and standards necessary to create an image database of multidetector-row computed tomography lung images as a resource for CAD researchers. To develop such a prospective database, its potential uses must be anticipated. The ultimate applications will influence the information that must be included along with the images, the relevant measures of algorithm performance, and the number of required images. In this article we outline assessment methodologies and statistical issues as they relate to several potential uses of the LIDC database. We review methods for performance assessment and discuss issues of defining "truth" as well as the complications that arise when truth information is not available. We also discuss issues about sizing and populating a database. (C) AUR, 2004.