A new approach to optimizing and evaluating computer-aided detection schemes

优化和评估计算机辅助检测方案的新方法

• 批准号: 9134748
• 负责人: ROBERT M NISHIKAWA
• 金额: $ 34.65万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9134748
• 关键词: Algorithms; Cancer Detection; Clinical; Clinical Research; Clinical effectiveness; Computer-Assisted Diagnosis; Data Set; Databases; Detection; Follow-Up Studies; Goals; Health; Image; Individual; Lead; Lesion; Malignant Neoplasms; Mammography; Measures; Methodology; Methods; Modality; Organ; Patient-Focused Outcomes; Performance; Reading; Research; Scheme; System; Testing; base; computer aided detection; design; improved; novel; novel strategies; radiologist; screening

DESCRIPTION (provided by applicant): The long-term objective of this research is to improve the clinical impact of computer-aided diagnosis systems. Specifically in this project, a calibrated data set will be developed that will be used to optimize a computer- aided detection (CADe) algorithm. Currently CADe algorithms are optimized for detecting cancers in images (so-called stand-alone performance, which is measured without considering the radiologist user). Our proposed method will maximize radiologists' performance in reading screening mammograms (i.e., CADe will be optimized for clinical benefit to the radiologist, not stand-alone performance) Two hypotheses will be tested: Hypothesis 1: Radiologists using a CADe scheme optimized using the calibrated dataset will have higher performance than when using a CADe scheme optimized using current methods; and Hypothesis 2: The improved performance of radiologists using an arbitrary CADe scheme, as measured in an observer study, can be predicted with sufficient accuracy using a calibrated dataset. This will be accomplished through the following specific aims: 1. Develop the calibrated database based on a group of radiologists reading without CADe and a group of radiologists analyzing individual CADe marks; 2. Validate the calibrated dataset through an observer study; and 3. Develop a novel method for optimizing CADe to maximize radiologists' performance. The calibrated dataset should improve the clinical effectiveness of CADe. Current clinical studies show that by using CADe, radiologists can increase their sensitivity for cancer detection by 10%. However, radiologists ignore up to 70% of correct CADe marked cancers. We believe that by optimizing CADe systems to maximize the benefit to the radiologist, as oppose to maximizing CADe performance without considering the effect on radiologists, will lead to larger gains in sensitivity by radiologists.

描述（由申请人提供）：本研究的长期目标是提高计算机辅助诊断系统的临床影响。具体来说，在这个项目中，经过校准的 将开发数据集，用于优化计算机辅助检测（CADe）算法。目前，CADe 算法针对检测图像中的癌症进行了优化（所谓的独立性能，在不考虑放射科医生用户的情况下进行测量）。我们提出的方法将最大限度地提高放射科医生在阅读筛查性乳房 X 光检查方面的表现（即，CADe 将针对放射科医生的临床效益进行优化，而不是独立性能）。将测试两个假设：假设 1：放射科医生使用使用校准后的 CADe 方案进行优化与使用当前方法优化的CADe方案相比，数据集将具有更高的性能；假设 2：使用任意 CADe 方案的放射科医生的性能改进（如观察者研究中测量的那样）可以使用校准数据集以足够的精度进行预测。这将通过以下具体目标来实现： 1. 根据一组放射科医生在没有使用 CADe 的情况下进行读数以及一组放射科医生分析各个 CADe 标记的情况，开发校准数据库； 2. 通过观察者研究验证校准数据集； 3. 开发一种优化 CADe 的新方法，以最大限度地提高放射科医生的绩效。校准数据集应提高 CADe 的临床有效性。目前的临床研究表明，通过使用 CADe，放射科医生可以将癌症检测的灵敏度提高 10%。然而，放射科医生忽略了高达 70% 正确的 CADe 标记癌症。我们相信，通过优化 CADe 系统来最大限度地提高放射科医生的利益，而不是在不考虑对放射科医生的影响的情况下最大化 CADe 性能，将导致放射科医生的灵敏度获得更大的提高。