Computer-Aided Diagnosis of Breast Lesions in Mammograms

描述（由申请人提供）：本申请广泛、长期 目的是通过检测乳腺癌， 一个早期和可治愈的阶段，并通过减少活检的数量良性 病变计算机辅助诊断（CAD）是指诊断过程，其中 放射科医生使用计算机分析乳房X光片作为诊断辅助， 实现更准确的解释。待检验的假设是， 优化和临床试验，将乳腺病变分类为 恶性或良性均可用于临床。具体目标是 （1）比较了两种计算机分类方法：一种是基于 一种是基于计算机提取的图像特征，另一种是基于乳腺成像的图像特征。 放射科医师提供的报告和数据系统（BI-RADS）病变描述符; (2)为放射科医师制定最佳策略，以便联合收割机将其诊断 （3）开展第二阶段临床试验， 并计划进行III期临床试验。的意义和 CAD对乳腺病变分类的健康相关性在于， 这可能有助于放射科医生减少良性病变的活检次数， 同时保持或增加乳房X线照相术的灵敏度。的 该项目的重要性和健康相关性在于，它将增加 通过优化提高CAD的临床有效性，将CAD从实验室中移出 从研究到临床评价，并开始临床试验过程， 最终决定CAD的临床疗效。研究设计是 优化以前开发的CAD方法，然后进行第二阶段 临床试验所用的方法包括病灶特征分析、人工神经网络分析、 人工神经网络，受试者工作特性 分析，观测器研究，关于理想观测器的数学建模 性能和Monte Carlo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.