Computer Aided Diagnostic Lung Cancer Detection Classification Computed Tomograph

描述（申请人提供）：肺癌的早期发现和诊断非常重要，因为这可能会改善肺癌患者的预后。本项目的目标是开发和评估一种集成的交互式计算机辅助诊断（CAD）方案，用于多层计算机断层扫描（CT）早期发现和诊断肺癌，该方案将检测方案和诊断方案结合到临床导向系统中。两套CAD系统的整合将为放射科医生提供一个高度实用和方便的诊断环境，与使用两套单独的CAD系统相比，它在技术上的可靠性和紧凑性也会得到提高。放射科医生和CAD方案之间的交互使放射科医生能够纠正计算机的检测错误，例如识别计算机遗漏的癌症，并忽略计算机错误检测到的大多数非结节。因此，通过使用集成的CAD方案，预计放射科医生在检测和诊断肺癌方面将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)一种基于规则的自动分类器，使用多个复合特征，最小化过度训练效应；(3)采用结节分割技术、基于规则的自动分类器和选择性结节增强滤波器的结节检测方案；(4)用于区分良性结节和非结节的三类结节诊断方案；(5)检测和诊断方案的集成，具有CAD方案与用户交互的能力。预计计算机化检测方案将检测出90%的结节，每次CT扫描的非结节少于4个，并且诊断方案将达到0.90或更高的Az值（接受者工作特征曲线下的面积），用于区分癌症和非癌症。最后，一项由20名参与的放射科医生进行的观察表现研究将检验综合交互式CAD方案的临床实用性。从观察者研究中可以预期，在交互式CAD方案的辅助下，放射科医师对肺癌的检测和诊断准确率将显著提高。

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.