Satisfaction of Search in Breast Cancer Detection

项目总结/摘要 乳腺癌在美国和世界各地的女性癌症发病率最高。尽管 技术进步-从电影屏幕图像到全视野数字乳腺X射线摄影（FFDM），现在又到 数字乳腺断层合成摄影（DBT）-每年的漏诊率一直保持稳定，范围在 筛选时为10-30%。技术本身并不能减少遗漏的错误;我们需要了解具体的 人类读者在解读图像时所面临的挑战，以及它们所导致的具体错误。 搜索满意度（SOS）是指在检测到病例中的第一个病变后， 在同一病例中其它病变的漏诊率显著升高。这种特殊的错误类型， 在已研究的放射学领域（包括胸部）， X射线摄影和计算机断层扫描。然而，它从未在乳腺癌领域进行过研究。 因此，迫切需要确定SOS如何导致乳腺癌筛查中的错误。 在本项目中，我们将确定SOS的发生率和根本原因， FFDM和DBT。我们设计了一种新的方法，克服了以前方法的局限性， 优化用于FFDM和DBT。以前研究SOS的方法包括摄影加法 人工病变的图像，这是不可行的乳房成像。相反，我们将建立一个数据库， 自然发生的案例是为研究SOS而设计的。这将涉及收集多个病变 例和对照单病灶病例，其中前者与后者在关键诊断上匹配 尺寸，如病变类型、病变大小和乳腺密度。在两个主要实验中（一个使用FFDM， 一个是DBT），放射科医生将从实验组中读取病例，标记位置和诊断， 良性和恶性病变。双病灶和单病灶病例的信号检测分析将用于 估计SOS的发生率。当参与者阅读每个病例时，将跟踪眼睛位置和瞳孔直径。 这些数据将使我们能够评估SOS的不同已知原因的流行程度： 终止，与单病变相比，首次病变检测后的搜索不太全面 对照组;（B）感知组，其中，在检测到第一病变后，参与者偏向于发现 随后的病变具有相似的感知特征，导致检测的灵敏度降低， 感知不同的目标;（c）资源枯竭，其中维护信息的需求 关于记忆中第一次检测到的损伤减少了可用的感知/认知资源，从而减少了 后续搜索的效率。了解乳腺癌中SOS的发生率和根本原因 检测将为计划的未来工作奠定基础，以制定培训计划和最佳实践， 减轻SOS错误的具体原因，从而降低乳腺癌筛查的漏诊率。

PROJECT SUMMARY/ABSTRACT Breast cancer has the highest incidence of cancer for women in the U.S. and across the world. Despite advances in technology—from film-screen images to Full Field Digital Mammography (FFDM) and now to Digital Breast Tomosynthesis (DBT)—the yearly miss rate has remained stubbornly stable, ranging between 10-30% at screening. Technology alone is not reducing errors of omission; we need to understand the specific challenges faced by the human readers interpreting the images, and the specific errors that they lead to. Satisfaction of Search (SOS) refers to the fact that, after having detected a first lesion in a case, the miss rate for additional lesions in the same case is substantially elevated. This specific type of error has been shown to account for 30% of misses in the domains of Radiology where it has been studied, including chest radiography and Computed Tomography. And yet, it has never been studied in the domain of breast cancer. Thus, there is a critical need to determine how SOS contributes to errors in breast cancer screening. In the present project we will determine the rates of occurrence and the underlying causes of SOS in FFDM and DBT. We have devised a novel method that overcomes limitations of previous methods and that is optimized for use in FFDM and DBT. Previous approaches to studying SOS involved the photographic addition of artificial lesions to images, which is not feasible for breast imaging. Instead, we will construct a database of naturally occurring cases that is structured for studying SOS. This will involve the collection of multiple-lesion cases and controlled single-lesions cases, where the former are matched with the latter on key diagnostic dimensions, such as lesion type, lesion size, and breast density. In two main experiments (one with FFDM and one with DBT), radiologists will read cases from the experimental set, marking the locations and diagnoses for benign and malignant lesions. Signal-detection analyses over dual- and single-lesion cases will be used to estimate the rate of SOS. Eye position and pupil diameter will be tracked as participants read each case. These data will allow us to assess the prevalence of different known causes of SOS: (a) premature termination, in which search following first lesion detection is less comprehensive compared with single-lesion control cases; (b) perceptual set, in which, after having detected a first lesion, participants are biased to find subsequent lesions with similar perceptual features, leading to reduced sensitivity in the detection of perceptually dissimilar targets; and (c) resource depletion, in which the demands of maintaining information about a first-detected lesion in memory reduce available perceptual/cognitive resources, thereby reducing the efficiency of subsequent search. Understanding the rates and underlying causes of SOS in breast cancer detection will lay the foundation for planned future work to develop training programs and best practices that mitigate the specific causes of SOS errors and thereby reduce miss rates in breast cancer screening.