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

描述(由申请人提供)：本研究的长期目标是改善计算机辅助诊断系统的临床影响。特别是在这个项目中，一个经过校准的 将开发用于优化计算机辅助检测(CADE)算法的数据集。目前，CADE算法被优化用于检测图像中的癌症(所谓的独立性能，其测量不考虑放射科医生用户)。我们建议的方法将最大限度地提高放射科医生阅读筛查乳房X光照片的性能(即，CADE将针对放射科医生的临床益处而进行优化，而不是独立的性能)。将测试两个假设：假设1：使用使用校准数据集优化的CADE方案的放射科医生将具有比使用使用当前方法优化的CADE方案更高的性能；以及假设2：可以使用校准数据集以足够的精度预测使用任意CADE方案的放射科医生在观察者研究中测量的改善的性能。这将通过以下具体目标来完成：1.基于一组不使用CADE阅读的放射科医生和一组分析单个CADE标记的放射科医生，开发校准数据库；2.通过观察者研究来验证校准数据集；以及3.开发一种新的方法来优化CADE以最大化放射科医生的表现。校正后的数据集应能提高CADE的临床效果。目前的临床研究表明，通过使用CADE，放射科医生可以将他们对癌症检测的敏感度提高10%。然而，放射科医生忽略了高达70%的正确CADE标记的癌症。我们相信，通过优化CADE系统以最大限度地为放射科医生带来好处，而不是在不考虑对放射科医生影响的情况下最大化CADE性能，将使放射科医生获得更大的敏感度。

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.