Sequential Reading Effects in Digital Breast Tomosynthesis

数字乳腺断层合成中的顺序读取效果

• 批准号: 10629384
• 负责人: Craig Kendall Abbey
• 金额: $ 45.02万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-14 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10629384
• 关键词: Affect; Area; Awareness; Breast Cancer Detection; Cancer Detection; Characteristics; Clinical; Clinical Data; Clinical Research; Clinical Trials; Communities; Data; Detection; Diagnostic; Digital Breast Tomosynthesis; Environment; Evaluation; Exposure to; Human Resources; Image; Image Analysis; Imaging Device; Imaging technology; Investments; Laboratories; Laboratory Study; Length; Mammographic screening; Mammography; Measurement; Medical Imaging; Medical center; Mining; Modeling; Modernization; Motivation; Nature; Nevada; Observational Study; Patients; Perception; Performance; Positioning Attribute; Protocols documentation; Publishing; Radiology Information Systems; Radiology Specialty; Reader; Reading; Research; Sampling; Scheme; Scientist; Sensory Process; Sorting; Statistical Data Interpretation; Statistical Models; Stimulus; Structure; System; Time; Translations; United States; Universities; Vision; Visual; Woman; breast density; breast imaging; case control; clinical imaging; clinical practice; density; design; digital; experience; imaging modality; imaging properties; improved; patient variability; product development; programs; prospective; radiologist; response; screening; tomosynthesis; vigilance; vision science; visual adaptation; visual processing

Project Summary/Abstract Digital breast tomosynthesis (DBT) has emerged as a viable imaging modality for breast cancer screening. But despite its improvements over standard mammography, the breast imaging community is well aware of its limitations. Roughly 40 million women in the United States have a screening mammogram each year, and the combination of a high-volume exam and less than ideal performance have made screening mammography an active area of research for many years. Prior studies have shown that reading breast screening images in batches improves performance, in terms of lower overall recall rates without significant change in the cancer detection rate. But no mechanism for this improvement has been demonstrated. A recently published clinical trial in the UK with over 1 million screening mammograms read by 360 practicing clinicians finds that screening performance actually improves as a reader progresses through a batch of mammograms. We hypothesize that, rather than vigilance, the operational mechanism affecting performance in batch reading is adaptation, which is consistent with improved performance over the course of a batch. We propose to characterize sequential reading effects in DBT images, to see if there is evidence to support our hypothesis that readers visually adapt to the statistical structure of the images as they read them. A few studies have shown adaptation effects in digital mammograms, but there have been no studies to date evaluating sequential effects in DBT. If adaptation is functioning in batch reading of DBT images, then there are a variety of was to take advantage of it to improve screening performance. We propose a thorough evaluation of sequential effects in DBT images that includes basic visual assessments of adaptation, statistical analysis of retrospective clinical data, and prospective analysis of performance in clinical reader studies. These will be analyzed to determine if image readers are consistent with the adaptation hypothesis, improved performance with depth in a batch, and modeled to see if there are additional factors that influence adaptation. Our project consists of three specific aims that cover these topics. Aim 1 implements a battery of visual assessments that define and characterize visual adaptation to DBT images. Aim 2 proposes mining clinical DBT reading data at the University of Pittsburgh Medical Center to evaluate retrospective performance detecting cancer (true-positive rates) and recalling patients (false-positive rates). This data will be modeled with factors that include adaptive sequential effects. Aim 3 will evaluate screening performance in DBT images in “laboratory” studies using clinical readers and cases that evaluate the effects of different batch length and the ordering of cases within a batch.

项目总结/摘要 数字乳腺断层合成摄影（DBT）已成为乳腺癌的一种可行的成像方式 筛选但是，尽管它比标准的乳房X光检查有所改进，乳房成像社区仍然是 很清楚它的局限性。在美国，大约有4000万妇女接受了乳房X光检查。 每年，高容量的考试和不太理想的表现相结合， 筛查乳房X线摄影是多年来的活跃研究领域。 先前的研究表明，批量阅读乳腺筛查图像可以提高性能， 总体召回率较低，但癌症检出率没有显著变化。但没有 这种改善的机制已经得到证实。最近在英国发表的一项临床试验， 由360名执业临床医生阅读的超过100万张筛查乳房X线照片发现， 实际上是随着读者阅读一批乳房X光片而改善的。我们假设， 与警觉相比，影响批量阅读绩效的运行机制是适应， 这与批次过程中改进的性能一致。 我们建议描述DBT图像中的顺序阅读效果，看看是否有证据表明 支持我们的假设，即读者在阅读时视觉上适应了图像的统计结构 他们一些研究表明数字乳房X光片具有适应效应，但还没有研究 迄今为止，评价DBT的顺序效应。如果自适应在DBT图像的批量阅读中起作用， 于是有多种办法来利用它来提高筛选性能。 我们提出了一个彻底的评估DBT图像，包括基本的视觉效果的顺序 适应性评估，回顾性临床数据的统计分析， 临床阅片师研究中的性能。将对这些进行分析，以确定图像读取器是否一致 与适应假设，提高了性能与深度在一批，并建模，看看是否有 是影响适应的其他因素。 我们的项目包括涵盖这些主题的三个具体目标。Aim 1实现了一组 视觉评估，定义和表征对DBT图像的视觉适应。目标2提出采矿 匹兹堡大学医学中心的临床DBT阅读数据，以评估回顾性 检测癌症（真阳性率）和召回患者（假阳性率）的性能。该数据 将使用包括自适应序列效应的因素进行建模。目标3将评估筛选 使用临床阅片师和评估效果的病例进行的“实验室”研究中DBT图像的性能 不同批次长度和批次内病例的排序。

项目成果

Visual adaptation to medical images: a comparison of digital mammography and tomosynthesis.

医学图像的视觉适应：数字乳房X线摄影和断层合成的比较。

• DOI: 10.1117/1.jmi.10.s1.s11909
• 发表时间: 2023
• 期刊: Journal of medical imaging (Bellingham, Wash.)
• 作者: Parthasarathy,MohanaKuppuswamy;Zuley,MargaritaL;Bandos,AndriyI;Abbey,CraigK;Webster,MichaelA
• 通讯作者: Webster,MichaelA