Computer Aided Diagnosis of Lung Cancer

简介(申请人提供)：肺癌是导致癌症的主要原因 男性和女性都有死亡。70%的五年存活率 当肺癌在局部阶段被诊断出来时，报告的比例为2 当发现远处转移时的百分比。最近的研究表明，螺旋 CT可能是一种有效的肺癌筛查工具。美国理工学院 放射成像网络(ACRIN)将开始一项随机对照试验 螺旋CT在肺癌筛查中的疗效评价。CT分析 对于放射科医生来说，图像检测肺结节是一项艰巨的任务。有些肺 结节可能会被忽视，因为大量的 需要解释的信息。对检测到的结核进行表征，以减少 不必要的活检也将变得更加重要，因为胸腔积液的数量 CT检查增多。计算机辅助诊断(CAD)可能是一种可行的方法 提高CT图像中肺癌检测的准确性和效率。它 如果实施CT肺癌筛查，这将特别有用。 拟议项目的目标是开发一种用于早期检测的CAD系统 胸部螺旋CT图像上的肺癌。我们假设一个准确的 CAD系统(1)可以开发，(2)可以作为辅助意见 放射科医生对胸部CT检查的解释，以及(3)将有所提高 放射科医生对肺癌检测的准确性。 我们将开发先进的计算机视觉技术来自动分割 螺旋CT图像，检测候选肺结节，鉴别结节和 正常的肺结构，并估计恶性变的可能性 结节。计算机化的图像分割和特征提取技术将 基于专家知识和图像特征进行开发。统计 将设计分类器、模糊分类器和人工神经网络 区分结节和正常结构，以及表征 恶性和良性结节。将进行定量CT体模研究 开发可靠的方法估计结核中的钙浓度 并估计CT图像上的结节体积，以便这些特征可以用于 我们用于恶性肿瘤检测的计算机辅助设计系统。观察者性能研究使用 将采用接收器操作特征(ROC)方法来 评价计算机辅助诊断对放射科医师诊断和分型的影响 CT表现为肺结节。将建立一个大型螺旋CT病例公共数据库 由NIH支持的财团收集的数据将成为 计算机视觉技术的发展。这个项目的创新之处 包括：(1)开发针对特定区域的计算机视觉检测方法 肺结节；(2)剔除氦中血管树为假 正向约简，(3)探索区间变化分析进行分类 恶性结节与良性结节的鉴别；(4)建立结节的定量检测方法 测量结节的钙浓度以改善其特征 钙化结节，以及(5)进行ROC研究，以评估CAD的能力 协助放射科医师在CT上发现和定性肺结节 学习。预计拟议的研究将产生有效的 肺癌诊断计算机辅助设计系统。当完全发育并在临床上 实施肺结节计算机辅助设计系统将提高肺部的疗效 利用螺旋CT进行癌症筛查，提高早期发现，提高患者的 患者的生存机会。

DESCRIPTION (provided by applicant): Lung cancer is the leading cause of cancer deaths in both men and women. A 70 percent five-year survival rate has been reported when the lung cancer is diagnosed at a local stage, compared to 2 percent when distant metastases are found. Recent studies indicate that helical CT may be an effective screening tool for lung cancer. The American College of Radiology Imaging Network (ACRIN) will begin a randomized controlled trial of helical CT for lung cancer screening to evaluate its efficacy. Analysis of CT images to detect lung nodules is a demanding task for radiologists. Some lung nodules will likely be overlooked because of the overwhelming amount of information to be interpreted. Characterization of detected nodules to reduce unnecessary biopsies will also become more important as the number of thoracic CT exams increases. Computer-aided diagnosis (CAD) can be a viable approach to improving the accuracy and efficiency of lung cancer detection in CT images. It will be particularly useful if lung cancer screening with CT is implemented. The goal of the proposed project is to develop a CAD system for early detection of lung cancers on thoracic helical CT images. We hypothesize that an accurate CAD system (1) can be developed, (2) can be used as a second opinion to assist radiologists in interpretation of thoracic CT exams, and (3) will improve radiologists' accuracy for lung cancer detection. We will develop advanced computer vision techniques to automatically segment helical CT images, detect candidate pulmonary nodules, differentiate nodule and normal pulmonary structures, and estimate the likelihood of malignancy of the nodules. Computerized image segmentation and feature extraction techniques will be developed based on expert knowledge and image characteristics. Statistical classifiers, fuzzy classifiers, and artificial neural networks will be designed to differentiate nodules and normal structures, as well as to characterize malignant and benign nodules. Quantitative CT phantom studies will be performed to develop reliable methods for estimating the calcium concentration of nodules and estimating nodule volume on CT images so that these features can be used in our CAD system for malignancy detection. Observer performance studies using receiver operating characteristic (ROC) methodology will be conducted to evaluate the effects of CAD on radiologists' detection and classification of lung nodules in CT images. A large public database of helical CT cases to be collected by an NIH-supported consortium will be the main data source for the development of the computer vision techniques. The innovations of this project include: (1) developing region-specific computer vision method for detection of lung nodules; (2) eliminating the vascular tree in the helium for false positive reduction, (3) exploring interval change analysis for classification of malignant and benign nodules; (4) developing a quantitative method for measuring the calcium concentration of nodules for improved characterization of calcified nodules, and (5) performing ROC studies to evaluate CAD's ability to assist radiologists in the detection and characterization of lung nodules in CT studies. It is expected that the proposed studies will result in an effective CAD system for lung cancer diagnosis. When fully developed and clinically implemented, a CAD system for lung nodules will increase the efficacy of lung cancer screening with helical CT, improve early detection, and improve the chance of survival of patients.