Sequential Reading Effects in Digital Breast Tomosynthesis

项目摘要/摘要 数字乳腺断层合成(DBT)已经成为乳腺癌的一种可行的成像方式 放映。但是，尽管它比标准的乳房X光检查有所改进，但乳房成像界 很清楚它的局限性。美国约有4000万女性接受乳房X光检查 每一年，大量的考试和不太理想的表现相结合 筛查乳房X光检查是多年来活跃的研究领域. 先前的研究表明，批量阅读乳房筛查图像可以提高性能，在 总体召回率较低，而癌症检测率没有明显变化。但不是的 这种改善的机制已经得到了证明。最近在英国发表的一项临床试验 360名执业临床医生阅读了100多万张筛查乳房X光照片，发现筛查性能 实际上，随着读者阅读一批乳房X光照片的进展，情况会有所改善。我们的假设是， 与警觉相比，影响批量阅读性能的运行机制是适应，即 与批处理过程中改进的性能保持一致。 我们建议表征DBT图像中的顺序读取效应，以查看是否有证据表明 支持我们的假设，即读者在阅读时在视觉上适应图像的统计结构 他们。一些研究已经显示了数字乳房X光检查中的适应效应，但还没有研究 到目前为止，在DBT中评估顺序效应。如果自适应在DBT图像的批量读取中起作用， 然后有各种方法可以利用它来提高筛选性能。 我们建议对DBT图像中的序列效应进行彻底的评估，包括基本视觉 适应评估，回顾临床数据的统计分析，以及前瞻性分析 在临床读者研究中的表现。将对这些进行分析，以确定图像读取器是否一致 在适应假设下，通过批处理中的深度改进性能，并对其进行建模以查看是否存在 是影响适应的其他因素。 我们的项目由三个具体目标组成，涵盖了这些主题。AIM 1实施了一系列 定义和表征对DBT图像的视觉适应的视觉评估。目标2建议挖掘 匹兹堡大学医学中心临床DBT读数数据的回顾评估 检测癌症(真阳性率)和召回患者(假阳性率)。此数据 将使用包括自适应顺序效果的因素进行建模。AIM 3将评估筛查 使用临床阅读器和评估效果的病例在实验室研究中的DBT图像的表现 不同的批次长度和批次内箱子的排序。

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.