Perception and Inter-Observer Variability in Mammography

乳腺 X 线摄影的感知和观察者间差异

• 批准号: 6924688
• 负责人: CLAUDIA R MELLO-THOMS
• 金额: $ 16.71万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2008-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6924688
• 关键词: artificial intelligence; bioimaging /biomedical imaging; breast neoplasm /cancer diagnosis; clinical research; diagnosis design /evaluation; diagnosis quality /standard; human data; human therapy evaluation; mammography; visual perception

DESCRIPTION (provided by applicant): Inter-observer variability in mammogram reading has been well documented in the literature. Various factors have been used to explain this variability; among them, the most significant are related to the management of perceived findings. However, the nature of this inter-observer variability has not been explored. Namely, were the lesions that were consistently reported by the radiologists any different from the ones that yield disagreement? Furthermore, could these differences be quantitatively assessed? Moreover, were these differences in any way related with the experience level of the observer? In addition, the interpretation of perceived findings is closely related with the visual search strategy used to scan the breast tissue, because observers compare perceived findings with the background, in order to determine their uniqueness. Hence, what is the effect of visual search strategy on inter-observer variability? Can this effect be modeled using Artificial Neural Networks (ANNs)? Can inferences be made regarding the observers' decision patterns by analyzing the results of simulations run on the ANNs? The work described here aims at answering these questions. We will use spatial frequency analysis to characterize the areas on mammogram cases where mammographers, chest radiologists with experience reading mammograms and radiology residents at the end of their mammography rotation, indicate the presence of a finding, or fail to do so. We will assess inter-observer agreement, as well as intra- and inter-group agreement for the various groups of observers. In addition, we will train artificial neural networks to represent each observer, in such a way that by changing the nature of the features input to the ANNs we will be able to simulate how such changes would have affected the actual observer. We will assess the effects on inter-observer variability of changing the search strategy used by the observer to sample the breast tissue. In our setting, the inter-observer variability will be assessed by comparing the outputs of the ANNs that represent each observer. In addition, the changes in sampling strategy will correspond to actual possible strategies for the human observers themselves.

描述（由申请人提供）： 乳房X线照片读数的观察者间差异已在文献中得到充分记录。多种因素被用来解释这种变异性；其中，最重要的是与感知结果的管理有关。 然而，这种观察者间差异的本质尚未得到探讨。也就是说，放射科医生一致报告的病变与产生分歧的病变有什么不同吗？此外，可以定量评估这些差异吗？此外，这些差异是否与观察者的经验水平有关？此外，对感知结果的解释与用于扫描乳腺组织的视觉搜索策略密切相关，因为观察者将感知结果与背景进行比较，以确定其独特性。那么，视觉搜索策略对观察者间变异性有何影响？可以使用人工神经网络 (ANN) 对这种效应进行建模吗？能否通过分析人工神经网络上运行的模拟结果来推断观察者的决策模式？ 这里描述的工作旨在回答这些问题。我们将使用空间频率分析来描述乳房 X 光检查病例中的区域，其中乳房 X 光检查医师、具有读取乳房 X 光照片经验的胸部放射科医生以及放射科住院医师在乳房 X 光检查轮换结束时指示存在发现或未能这样做。我们将评估观察员间的一致性，以及各观察员组的组内和组间一致性。此外，我们将训练人工神经网络来代表每个观察者，通过改变输入到人工神经网络的特征的性质，我们将能够模拟这种变化如何影响实际的观察者。我们将评估改变观察者用于对乳腺组织进行采样的搜索策略对观察者间变异性的影响。在我们的设置中，观察者间的变异性将通过比较代表每个观察者的人工神经网络的输出来评估。此外，采样策略的变化将与人类观察者本身实际可能的策​​略相对应。