Optimization of CAD Output in Breast Imaging

描述（由申请人提供）：虽然许多研究者在开发计算机辅助检测和诊断（CAD）病变的方法方面取得了很大进展，但目前用于将计算机输出传达给用户的人机界面不足。帮助放射科医生诊断癌症的智能工作站利用对病变的恶性概率的估计，通常通过在独立数据库上训练分类器来获得。恶性肿瘤概率的这些估计取决于训练数据库中癌症的患病率，其通常不对应于用户经历的人群中癌症的患病率，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肿瘤概率。该建议旨在扩展我们的智能工作站，包括转换的计算机输出，无论是读者的具体或放射实践的具体。该研究的具体目的是（1）收集放射科医生在临床乳房X线照片和超声照片的数据库上对恶性肿瘤概率的评估的评级数据，（2）开发模型，利用该模型将计算机输出转换为将“匹配”阅片者的内部参数的模型，（3）使用两种模态的计算机和人类数据来比较模型，（4）在我们的临床有用接口的设计中使用变换的结果来修改计算机输出，以及（5）使用针对特定放射科医师或特定放射学实践定制的增强智能接口来执行CAD的第一测试。拟议的研究是高度相关的，这种增强的人机界面，预计将改善和加快使用计算机辅助乳腺癌成像和解释。

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.