Brunyé Tad T, Mercan Ezgi, Weaver Donald L, Elmore Joann G
Center for Applied Brain & Cognitive Sciences, Tufts University, Medford, MA, United States.
Department of Computer Science and Engineering, University of Washington, Seattle, WA, United States.
J Biomed Inform. 2017 Feb;66:171-179. doi: 10.1016/j.jbi.2017.01.004. Epub 2017 Jan 10.
Digital whole slide imaging is an increasingly common medium in pathology, with application to education, telemedicine, and rendering second opinions. It has also made it possible to use eye tracking devices to explore the dynamic visual inspection and interpretation of histopathological features of tissue while pathologists review cases. Using whole slide images, the present study examined how a pathologist's diagnosis is influenced by fixed case-level factors, their prior clinical experience, and their patterns of visual inspection. Participating pathologists interpreted one of two test sets, each containing 12 digital whole slide images of breast biopsy specimens. Cases represented four diagnostic categories as determined via expert consensus: benign without atypia, atypia, ductal carcinoma in situ (DCIS), and invasive cancer. Each case included one or more regions of interest (ROIs) previously determined as of critical diagnostic importance. During pathologist interpretation we tracked eye movements, viewer tool behavior (zooming, panning), and interpretation time. Models were built using logistic and linear regression with generalized estimating equations, testing whether variables at the level of the pathologists, cases, and visual interpretive behavior would independently and/or interactively predict diagnostic accuracy and efficiency. Diagnostic accuracy varied as a function of case consensus diagnosis, replicating earlier research. As would be expected, benign cases tended to elicit false positives, and atypia, DCIS, and invasive cases tended to elicit false negatives. Pathologist experience levels, case consensus diagnosis, case difficulty, eye fixation durations, and the extent to which pathologists' eyes fixated within versus outside of diagnostic ROIs, all independently or interactively predicted diagnostic accuracy. Higher zooming behavior predicted a tendency to over-interpret benign and atypia cases, but not DCIS cases. Efficiency was not predicted by pathologist- or visual search-level variables. Results provide new insights into the medical interpretive process and demonstrate the complex interactions between pathologists and cases that guide diagnostic decision-making. Implications for training, clinical practice, and computer-aided decision aids are considered.
数字全切片成像在病理学中越来越普遍,应用于教育、远程医疗和提供二次诊断意见。它还使得在病理学家查看病例时,能够使用眼动追踪设备来探索对组织病理特征的动态视觉检查和解读。本研究使用全切片图像,考察了病理学家的诊断如何受到固定的病例水平因素、他们先前的临床经验以及他们的视觉检查模式的影响。参与研究的病理学家解读了两个测试集中的一个,每个测试集包含12张乳腺活检标本的数字全切片图像。病例代表了通过专家共识确定的四种诊断类别:无异型增生的良性病变、异型增生、导管原位癌(DCIS)和浸润性癌。每个病例都包括一个或多个先前确定为具有关键诊断重要性的感兴趣区域(ROI)。在病理学家解读过程中,我们追踪了眼动、查看工具行为(缩放、平移)和解读时间。使用广义估计方程的逻辑回归和线性回归建立模型,测试病理学家、病例和视觉解读行为水平的变量是否会独立和/或交互地预测诊断准确性和效率。诊断准确性因病例共识诊断而异,重复了早期研究。正如预期的那样,良性病例往往会引发假阳性结果,而异型增生、DCIS和浸润性病例往往会引发假阴性结果。病理学家的经验水平、病例共识诊断、病例难度、注视持续时间以及病理学家的眼睛在诊断ROI内与外的注视程度,都独立或交互地预测了诊断准确性。更高的缩放行为预示着对良性和异型增生病例过度解读的倾向,但对DCIS病例则不然。效率并未由病理学家或视觉搜索水平的变量预测。研究结果为医学解读过程提供了新的见解,并展示了病理学家与病例之间指导诊断决策的复杂相互作用。同时考虑了对培训、临床实践和计算机辅助决策辅助工具的影响。