Satisfaction of Search in Breast Cancer Detection

乳腺癌检测搜索满意度

• 批准号: 10183647
• 负责人: Andrew R Hollingworth
• 金额: $ 67.31万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-15 至 2026-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10183647
• 关键词: Benign; Breast Cancer Detection; Caliber; Cause of Death; Chest; Cognitive; Collaborations; Collection; Data; Databases; Detection; Development; Diagnosis; Diagnostic; Digital Breast Tomosynthesis; Digital Mammography; Dimensions; Disease; Eye; Film; Foundations; Future; Goals; High Prevalence; Human; Image; Impairment; Incidence; Institution; Investigation; Knowledge; Lead; Lesion; Location; Malignant - descriptor; Malignant Neoplasms; Mammography; Measures; Medical Imaging; Memory; Methods; Modality; Participant; Positioning Attribute; Prevalence; Probability; Pupil; ROC Curve; Radiology Specialty; Reader; Reading; Reporting; Research; Research Personnel; Resources; Screening for cancer; Signal Detection Analysis; Source; Stimulus; Structure; Techniques; Technology; Thoracic Radiography; Three-Dimensional Image; Training; Training Programs; Triplet Multiple Birth; Visual; Woman; Work; X-Ray Computed Tomography; X-Ray Tomography; base; breast cancer diagnosis; breast density; breast imaging; cancer type; case control; cognitive capacity; expectation; experimental study; imaging modality; improved; interest; malignant breast neoplasm; novel; premature; programs; radiologist; response; satisfaction; screening; standard of care; visual search; visual tracking; Benign; Breast Cancer Detection; Caliber; Cause of Death; Chest; Cognitive; Collaborations; Collection; Data; Databases; Detection; Development; Diagnosis; Diagnostic; Digital Breast Tomosynthesis; Digital Mammography; Dimensions; Disease; Eye; Film; Foundations; Future; Goals; High Prevalence; Human; Image; Impairment; Incidence; Institution; Investigation; Knowledge; Lead; Lesion; Location; Malignant - descriptor; Malignant Neoplasms; Mammography; Measures; Medical Imaging; Memory; Methods; Modality; Participant; Positioning Attribute; Prevalence; Probability; Pupil; ROC Curve; Radiology Specialty; Reader; Reading; Reporting; Research; Research Personnel; Resources; Screening for cancer; Signal Detection Analysis; Source; Stimulus; Structure; Techniques; Technology; Thoracic Radiography; Three-Dimensional Image; Training; Training Programs; Triplet Multiple Birth; Visual; Woman; Work; X-Ray Computed Tomography; X-Ray Tomography; base; breast cancer diagnosis; breast density; breast imaging; cancer type; case control; cognitive capacity; expectation; experimental study; imaging modality; improved; interest; malignant breast neoplasm; novel; premature; programs; radiologist; response; satisfaction; screening; standard of care; visual search; visual tracking

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.

项目摘要/摘要 乳腺癌是美国和世界各地女性的癌症事件最高。尽管 技术进步 - 从电影屏幕图像到完整的现场数字乳房X线摄影（FFDM），现在 数字乳房断层合成（DBT） - 每年的失误率一直保持稳定，范围 筛查时10-30％。仅技术并不能减少遗漏的错误；我们需要了解特定的 人类读者解释图像及其导致的特定错误所面临的挑战。 搜索（SOS）的满意度是指以下事实：在检测到第一个病变后， 在同一情况下，其他病变的错率显着升高。这种特定类型的错误已经 显示出已研究的放射学领域的30％的错过，包括胸部 放射线照相和计算机断层扫描。然而，它从未在乳腺癌领域进行研究。 这是确定SOS如何导致乳腺癌筛查中的错误的迫切需要。 在本项目中，我们将确定发生的发生率和SOS中的基本原因 FFDM和DBT。我们设计了一种新的方法，可以克服以前方法的局限性，也就是说 优化用于FFDM和DBT。研究SOS的先前方法涉及摄影 图像的人工病变，这对于乳房成像是不可行的。相反，我们将构建一个数据库 天然存在用于研究SOS的病例。这将涉及多重化的收集 病例和受控的单个病例，其中前者与钥匙诊断的后者匹配 尺寸，例如病变类型，病变大小和乳房密度。在两个主要实验中（一个带有FFDM和 放射科医生将读取实验集的病例，标志着一个案例 良性和恶性病变。双重和单膜病例的信号检测分析将用于 估计SOS的速率。当参与者阅读每种情况时，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的不同已知原因的患病率：（a）过早 终止，与单质量相比，在初次病变检测之后搜索不全面 控制案件； （b）感知集，其中，在检测到第一个病变后，参与者有偏见 随后具有相似感知特征的病变，导致检测的灵敏度降低 感知上不同的目标； （c）资源部署，其中维护信息的需求 关于记忆中的第一个探测病变，可减少可用的感知/认知资源，从而减少 随后的搜索效率。了解乳腺癌中SOS的速度和根本原因 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错误的特定原因，从而降低乳腺癌筛查中的错率。