Optimization of CAD Output in Breast Imaging

描述(由申请人提供)：虽然许多研究人员在开发用于病变的计算机辅助检测和诊断(CAD)的方法方面取得了很大进展，但当前用于将计算机输出信息传达给用户的人机界面是不够的。帮助放射科医生诊断癌症的智能工作站利用对病变恶性概率的估计，通常通过在独立数据库上训练分类器来获得。对恶性肿瘤概率的这些估计依赖于训练数据库中的癌症流行，这最常不对应于用户具有经验的人群中的癌症流行，例如，在用户的医疗实践中看到的人群。因此，用户通常难以解释计算机估计的恶性概率。这项提议旨在扩展我们的智能工作站，以包括计算机输出的转换，即特定于读者或特定于放射学实践的转换。这项研究的具体目的是(1)收集放射科医生对临床乳房X光照片和超声图像数据库的评级数据，根据他们对恶性肿瘤概率的评估，(2)开发模型，用于将计算机输出转换为那些将与阅读器的内部参数“匹配”的模型，(3)使用计算机和人体数据跨两种模式比较模型，(4)使用转换的结果来修改临床有用界面的设计中的计算机输出，以及(5)使用为特定放射科医生或特定放射学实践定制的增强型智能界面执行第一次CAD测试。这项拟议的研究具有很高的相关性，因为这种增强的人机界面有望改善和加快计算机辅助手段在乳腺癌成像和解释中的使用。

DESCRIPTION (provided by applicant): While many investigators have made great progress in developing methods for computer-aided detection and diagnosis (CAD) of lesions, current human interfaces for communicating the computer output to the user are inadequate. Intelligent workstations that aid radiologists in diagnosing cancer utilize an estimate of a lesion's probability of malignancy, usually obtained by training a classifier on an independent database. These estimates of the probability of malignancy are dependent on the prevalence of cancer in the training database, which most often does not correspond to the prevalence of cancer in the population from which the user has experience, e.g., the population seen in the user's medical practice. Thus, the user often has difficulty interpreting the computer-estimated probability of malignancy. This proposal aims to extend our intelligent workstation to include a transformation of the computer output that is either reader specific or radiology-practice specific. The specific aims of the study are (1) collect radiologists' rating data on a database of clinical mammograms and sonograms in terms of their assessment of the probability of malignancy, (2) develop models with which to transform computer output to those that would "match" the internal parameters of the reader, (3) compare the models using both the computer and human data across the two modalities, (4) use the results of the transformation to modify computer output in our design of clinically useful interfaces, and (5) perform the first test of CAD using enhanced intelligent interfaces customized to the particular radiologist or the particular radiology practice. The proposed study is highly relevant in that such enhanced human computer interfaces are expected to improve and expedite the use of computer aids in breast cancer imaging and interpretation.