Computer Aided Diagnostic Lung Cancer Detection Classification Computed Tomograph

描述（由申请人提供）：肺癌的早期检测和诊断非常重要，因为它们可以改善肺癌患者的预后。我们在这个项目中的目标是开发和评估一个集成的交互式计算机辅助诊断（CAD）计划，用于早期检测和诊断肺癌的多层计算机断层扫描（CT），这将结合到一个面向临床的系统的检测方案和诊断方案。两种CAD方案的集成将为放射科医师提供高度实用和方便的诊断环境，并且与使用两种单独的CAD方案相比，它还将提供技术上改进的可靠性和紧凑性。放射科医师和CAD方案之间的交互使得放射科医师能够校正计算机的检测错误，例如识别计算机遗漏的癌症，并且忽略计算机错误检测到的大部分非结节。因此，预计放射科医师将通过使用集成CAD方案在肺癌的检测和诊断方面变得更加准确和高效。本项目将开发许多创新技术和方案，包括（1）基于螺旋扫描和动态规划的精确分割技术;（2）使用多个复合特征的自动规则分类器，最大限度地减少过度训练效应;（3）使用结节分割技术、自动规则分类器和选择性结节增强滤波器的结节检测方案;（4）用于区分癌症与良性结节和非结节的三类结节诊断方案;以及（5）检测和诊断方案的集成，具有CAD方案与其用户之间的交互能力。预计计算机化检测方案将检测90%的结节，每次CT扫描少于4个非结节，并且诊断方案将实现0.90或更高的Az值（接受者操作特征曲线下的面积）以区分癌症和非癌症。最后，将与20名参与放射科医生进行观察员性能研究，以检查集成交互式CAD方案的临床实用性。从观察者研究中可以预期，放射科医生将在交互式CAD方案的帮助下显著提高肺癌的检测和诊断准确性。

DESCRIPTION (provided by applicant): Early detection and diagnosis of lung cancer are very important because they may lead to an improved prognosis for lung cancer patients. Our goal in this project is to develop and evaluate an integrated interactive computer-aided diagnostic (CAD) scheme for early detection and diagnosis of lung cancer in multi-slice computed tomography (CT), which will incorporate a detection scheme and a diagnosis scheme into a clinically oriented system. The integration of the two CAD schemes will provide radiologists with a highly practical and convenient diagnostic environment, and it will also provide technically improved reliability and compactness compared with the use of two separate CAD schemes. The interaction between radiologists and a CAD scheme enables radiologists to correct the computer's detection errors, such as to identify cancers missed by the computer, and to ignore most of the non-nodules incorrectly detected by the computer. As a result, it is expected that radiologists would become more accurate and productive in both detection and diagnosis of lung cancers by use of the integrated CAD scheme. Many innovative techniques and schemes are to be developed in this project, including (1) an accurate segmentation technique based on spiral scanning and dynamic programming; (2) an automated rule-based classifier using multiple composite features with minimized overtraining effects; (3) a nodule detection scheme using the nodule segmentation technique, the automated rule-based classifier, and a selective nodule enhancement filter; (4) a three-category nodule diagnosis scheme for distinguishing cancers from benign nodules and nonnodules; and (5) integration of the detection and diagnosis schemes, with the capability of interaction between the CAD scheme and its users. It is anticipated that the computerized detection scheme will detect 90% of nodules, with less than 4 non-nodules per CT scan, and that the diagnosis scheme will achieve an Az value (area under the receiver operating characteristic curve) of 0.90 or higher for distinction between cancers and non-cancers. Finally, an observer performance study will be conducted with 20 participating radiologists to examine the clinical usefulness of the integrated interactive CAD scheme. It is expected from the observer study that radiologists would significantly improve their detection and diagnosis accuracy for lung cancer with the aid of the interactive CAD scheme.