Computer-Aided Diagnosis of Breast Lesions in Mammograms

该应用程序的广泛，长期目标是通过早期治疗减少乳腺癌的疾病负担。 检测和准确的诊断，从而导致有效的治疗。该项目的目标是开发 用于乳腺癌检测的乳腺钙化计算机辅助诊断（CADx）的创新方法 与诊断我们小组的其他研究人员正在单独开发乳腺肿块的CADx， 由于乳腺肿块的诊断检查需要多模态成像， （超声波和MRI）除了乳房X光检查。我们将检验乳腺CADx 钙化可以帮助改善乳腺癌的检测和诊断。该项目的具体目标是： (1)减少或消除放射科医师之间和放射科医师内部的差异对计算机分类的影响， 与计算机的交互;（2）研究放大乳腺X线照片的计算机分类;（3） 研究并减少多视图中病变分类的计算机可变性;以及（4）研究 CADx在增强计算机辅助检测（CADe）筛查有效性方面的潜在益处 乳房X光检查研究设计将是了解计算机可变性的机制， 计算从确定的来源，开发新的技术，以尽量减少计算机计算 可变性，为新的CADx应用开发新技术，并执行观察员性能 研究表明，CADx可以潜在地增强CADe的有效性。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 性能研究实现这些目标的理由包括预期需要解决关键的 基于我们的CADx技术已经证明的高性能，CADx的临床接受问题 和计划的CADx临床前和临床评价，以及潜在的新的机会， 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 在以前的研究中，或基于理论研究，或基于我们在与 放射科医生开发CADx技术。所述研究的重要性和健康相关性 它将解决当前CADx技术的重大局限性，这些局限性可能会阻碍 CADx的临床接受度，并将解决CADx的创新潜力，以提高 乳腺癌的早期诊断如果实现了应用的目标，CADx将进一步发展 在技术上，将变得更容易被放射科医生在临床上接受：一旦CADx的临床受益 研究表明，CADx将成为乳腺癌检测和诊断的重要新临床工具。

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.