Computer-Aided Diagnosis of Breast Lesions in Mammograms

该应用的广泛、长期目标是通过早期治疗减轻乳腺癌的疾病负担。 检测和准确的诊断，这将导致有效的治疗。这个项目的目标是开发 用于乳腺癌检测的乳房钙化计算机辅助诊断(CADx)的创新方法 和诊断。我们团队中的其他研究人员正在单独开发用于乳腺肿块的CADx，在 与该项目相结合，因为乳房肿块的诊断检查需要多模式成像 (超声波和核磁共振)，除了乳房X光检查。我们将检验这样的假设：乳房的CADx 钙化有助于提高乳腺癌的检测和诊断水平。该项目的具体目标是： (1)减少或消除放射科医生之间和内部的可变性对计算机分类的影响 与计算机的交互；(2)研究放大乳房X光片的计算机分类；(3) 在多个视图中调查和减少病变分类中的计算机可变性；以及(4)调查 CADx对提高计算机辅助检测(CADE)筛查效果的潜在益处 乳房X光检查。研究设计将理解计算机在ITS中的可变性的机制 从已确定的来源进行计算，以开发新的技术来最大限度地减少计算机计算 可变性，为新的CADx应用程序开发新技术，并执行观察者性能 研究表明，CADx可以潜在地增强CADE的有效性。将使用的方法包括 计算机图像分析、统计分类器、ROC分析、统计比较和观察者 性能研究。追求这些目标的理由包括预期需要解决关键问题 基于我们的CADx技术已经证明的高性能的CADx的临床接受问题 以及对CADx的计划的临床前和临床评估，以及潜在新药的新机会 应用CADx提高CADE的有效性。我们将使用的技术要么是经过验证的 在以往的调查中，或基于理论研究，或基于我们在工作中的观察 放射科医生正在开发CADx技术。所描述的研究的重要性和与健康的相关性 在本应用中，它将解决当前CADx技术可能阻碍 CADx的临床接受性，并将解决CADx的创新潜力，以增强 CADE在乳腺癌检测中的应用。如果实现了应用的目标，CADx将得到推进 在技术上，并将变得更容易为放射科医生临床接受：一旦CADx的临床好处是 实践证明，CADx将成为乳腺癌检测和诊断的一种重要的临床新工具。

The application's broad, long-term objective is to reduce the disease burden of breast cancer through early detection and accurate diagnosis, which lead to effective treatment. The goal of this project is to develop innovative approaches to computer-aided diagnosis (CADx) of breast calcifications for breast cancer detection and diagnosis. Other researchers in our group are developing CADx for breast masses separately, in conjunction with this project, because diagnostic workup of breast masses requires multi-modality imaging (ultrasound and MRI) in addition to mammography. We will test the hypothesis that CADx of breast calcifications can help improve breast cancer detection and diagnosis. The Specific Aims of this project are: (1) reduce or eliminate influence on computer classification from inter- and intra-radiologist variability in their interaction with the computer; (2) investigate computer classification of magnification mammogram; (3) investigate and reduce computer variability in classification of a lesion in multiple views; and (4) investigate potential benefit of CADx to enhance the effectiveness of computer-aided detection (CADe) in screening mammography. The research design will be to understand the mechanisms of computer variability in its calculations from the identified sources, to develop new techniques to minimize computer calculation variability, to develop new techniques for new CADx applications, and to perform an observer performance study to show that CADx can potentially enhance the effectiveness of CADe. The methods to be used include computer image analysis, statistical classifier, ROC analysis, statistical comparison, and observer performance study. The rationales for pursuing these goals include an anticipated need to address critical clinical acceptance issues of CADx based on already demonstrated high performance of our CADx technique and a planned pre-clinical and clinical evaluation of CADx, and a new opportunity for potential novel application of CADx to enhance the effectiveness of CADe. The techniques that we will use are either proven in previous investigations, or based on theoretical studies, or based on our observation in working with radiologists developing CADx techniques. The importance and health relatedness of the research described in this application is that it will address significant limitations of current CADx technique that likely will hinder clinical acceptance of CADx and will address an innovative potential for CADx to enhance the effectiveness of CADe in breast cancer detection. If the Aims of the application are achieved, CADx will be advanced technologically and will become more clinically acceptable to radiologists: once clinical benefit of CADx is demonstrated, CADx will become an important new clinical tool for breast cancer detection and diagnosis.