Computer-aided decision model for interpreting breast MR

用于解读乳腺 MR 的计算机辅助决策模型

• 批准号: 6872151
• 负责人: ALAN I. PENN
• 金额: $ 63.89万
• 依托单位: ALAN PENN AND ASSOCIATES, INC.
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-01-01 至 2007-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6872151
• 关键词: bioengineering /biomedical engineering; bioimaging /biomedical imaging; breast neoplasm /cancer diagnosis; breast neoplasms; clinical research; computer assisted diagnosis; computer simulation; computer system design /evaluation; decision making; female; functional magnetic resonance imaging; human data; image processing; mammography; mathematical model; mathematics; statistics /biometry; women' s health

DESCRIPTION (provided by applicant): The overall goal of this project is to develop, evaluate and commercialize a computer-aided-diagnosis (CAD) system that supports an enhanced decision model for interpreting breast MR images. The proposed system will enable clinicians to more readily incorporate breast MR into their decision-making and patient care. The specific aim is to develop an integrated methodology to improve sensitivity rates of radiologists with minimal clinical experience in breast MR to levels that approximate sensitivity rates of radiologists with extensive experience in breast MR. The integrated methodology has three components: 1) Enhance the interpretation model previously proposed by Nunes, Schanll, et al. to contain additional decision nodes based on lesion density and kinetics. 2) Provide readers with a CAD system that displays quantitative and visualization aids to assist in the interpretation at the decision nodes. 3) Generate training tools and case studies for teaching radiologists how to interpret and identify lesion characteristics. The CAD system is based on the statistical surface fractal features and statistical border fractal features developed in prior research. The CAD system will be developed and tested using MR image data from a consortium of institutions that use different protocols and MR systems. A reader study will evaluate changes in interpretive and diagnostic performance of radiologists who have minimal clinical experience interpreting breast MR when they are provided with CAD support.

描述（由申请人提供）：本项目的总体目标是开发、评估和商业化计算机辅助诊断（CAD）系统，该系统支持用于解释乳腺MR图像的增强决策模型。拟议的系统将使临床医生更容易将乳腺MR纳入他们的决策和病人护理。具体目标是开发一种综合方法，以提高具有最少乳腺MR临床经验的放射科医生的敏感率，使其接近具有丰富乳腺MR经验的放射科医生的敏感率。综合方法有三个组成部分：1）增强Nunes、Schanll等人先前提出的解释模型，以包含基于病变密度和动力学的额外决策节点。2)为读者提供一个CAD系统，显示定量和可视化的帮助，以帮助在决策节点的解释。3)生成培训工具和案例研究，用于教授放射科医生如何解释和识别病变特征。该CAD系统是基于统计表面分形特征和统计边界分形特征在以前的研究发展。CAD系统将使用来自使用不同协议和MR系统的机构联盟的MR图像数据进行开发和测试。读片医师研究将评价在提供CAD支持时，具有最少临床经验的放射科医师解读乳腺MR的解读和诊断性能的变化。