Computer Aided Diagnosis of Lung Cancer

描述（由申请人提供）：肺癌是癌症的主要原因 男性和女性的死亡。百分之七十的五年存活率 当肺癌在局部阶段被诊断时，报告，相比之下， 最近的研究表明，当发现远处转移时， CT可能是一种有效的肺癌筛查工具。The American College of 放射成像网络（ACRIN）将开始一项随机对照试验， 螺旋CT在肺癌筛查中的应用价值。分析CT 用于检测肺结节的图像对于放射科医师来说是一项要求很高的任务。一些肺 结核可能会被忽视，因为大量的 需要解释的信息。对检测到的结核进行表征， 不必要的活组织检查也将变得更加重要， CT检查增加。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 将特别有用，如果肺癌筛查与CT实施。 拟议项目的目标是开发一个用于早期检测的CAD系统 肺癌的胸部螺旋CT图像。我们假设一个准确的 CAD系统（1）可开发，（2）可作为第二意见辅助 放射科医生在胸部CT检查的解释，和（3）将提高 放射科医生检测肺癌的准确性。 我们将开发先进的计算机视觉技术， 螺旋CT图像，检测候选肺结节，区分结节， 正常的肺结构，并估计恶性肿瘤的可能性， 结节计算机图像分割和特征提取技术将 基于专家知识和图像特征开发。统计 分类器、模糊分类器和人工神经网络将被设计 以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 制定可靠的方法，估计结核中的钙浓度 以及估计CT图像上的结节体积，使得这些特征可以用于 我们的恶性肿瘤检测CAD系统。观察员性能研究， 将进行受试者工作特征（ROC）方法， 评估CAD对放射科医生检测和分类的影响， CT图像中的肺结节。一个大型的公共螺旋CT病例数据库， 由NIH支持的联盟收集的数据将是本研究的主要数据来源。 计算机视觉技术的发展。该项目的创新之处在于 包括：（1）开发用于检测 肺结节;（2）排除氦中血管树， 正约简，（3）探索区间变化分析分类 恶性和良性结节;（4）建立一种定量方法， 测量结核的钙浓度， 钙化结节，和（5）进行ROC研究，以评估CAD的能力， 协助放射科医生在CT中检测和表征肺结节 问题研究预计拟议的研究将产生有效的 肺癌诊断CAD系统。当完全发育和临床上 实施，肺结节的CAD系统将提高肺结节的疗效。 通过螺旋CT进行癌症筛查，提高早期发现率， 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.