Computer-Aided Diagnosis of Breast Lesions in Mammograms

描述(由申请人提供)：这项申请的广泛、长期目标是通过检测乳腺癌来减轻疾病负担 早期和可治愈的阶段，并通过减少良性病变的活检数量 损伤。计算机辅助诊断(CAD)指的是一种诊断过程，其中 放射科医生使用计算机分析乳房X光照片作为诊断的辅助手段 实现更精准的解读。需要检验的假设是， 优化和临床试验，将乳腺病变分类为 临床上可以使用恶性或良性。这样做的具体目的是 应用有：(1)比较两种计算机分类方法：一种基于 计算机图像特征提取与基于乳腺成像的图像特征研究 放射科医生提供的报告和数据系统(BI-RADS)损伤描述符； (2)为放射科医生制定最佳策略，以结合他们的诊断 计算机评估；(3)开展二期临床 并计划进行第三阶段临床试验。它的意义和 用于乳房病变分类的CAD与健康的相关性在于它可以 可能有助于放射科医生减少良性病变的活检次数 同时保持或提高乳房X光照相的灵敏度。这个 这个项目的意义和与健康相关的是它将增加 优化CAD临床效果，让CAD走出实验室 研究到临床评估，并启动临床试验过程，将 最终决定冠心病的临床疗效。研究设计是为了 优化以前开发的CAD方法，然后进行第二阶段 临床试验。将使用的方法包括病变特征分析、ARTIF 关于神经网络(ANN)、接收器工作特性(ROC) 关于理想观察者的分析、观察者研究、数学建模 性能，并进行蒙特卡罗模拟。

DESCRIPTION (provided by applicant): This application's broad, long-term objective is to lessen the disease burden of breast cancer by detecting it at an early and curable stage and by reducing the number of biopsies on benign lesions. Computer-aided diagnosis (CAD) refers to a diagnostic process in which a radiologist uses a computer analysis of a mammogram as a diagnostic aid to achieve more accurate interpretation. The hypothesis to be tested is that with optimization and clinical trial, CAD methods that classify breast lesions as malignant or benign can be used clinically. The specific aims of this application are: (1) To compare two computer classification methods: one based on image features extracted by a computer and one based on the Breast Imaging Report and Data System (BI-RADS) lesion descriptors provided by radiologists; (2) To develop optimal strategies for radiologists to combine their diagnostic assessment with that of the computer; (3) To carry out a Phase II clinical trial and to plan a Phase III clinical trial. The significance and health-relatedness of CAD for breast lesion classification is that it can potentially help radiologists reduce the number of biopsies on benign lesions while maintaining or increasing the sensitivity of mammography. The significance and health-relatedness of this project is that it will increase clinical effectiveness of CAD through optimization, move CAD from laboratory research to clinical evaluation, and start a clinical trial process that will ultimately determine CAD's clinical efficacy. The research design is to optimize previously developed CAD methods and then to conduct a Phase II clinical trial. The methods to be used include lesion feature analysis, artif aboutcial neural networks (ANNs), receiver operating characteristic (ROC) analysis, observer study, mathematical modeling with respect to ideal observer performance, and Monte Carlo simulation.