Image Optimisation and Perception Group (MIOPeG), Discipline of Medical Imaging and Radiation Sciences, The University of Sydney, Sydney, NSW, Australia.
Medical Imaging Department, Prince of Wales Hospital, Randwick, NSW, Australia.
Med Phys. 2018 Jul;45(7):3052-3062. doi: 10.1002/mp.12935. Epub 2018 May 16.
The purpose of this study was to Propose a classifier based on recurrence quantification analysis (RQA) metrics for distinguishing experts' scanpaths from those of less-experienced readers and to explore the association of spatiotemporal dynamics of the mammographic scanpaths with the characteristics of cases and radiologists using RQA metrics.
Eye movements were recorded from eight radiologists (two cohorts: four experienced and four less-experienced) while reading 120 mammograms (59 cancer, 61 normal). Ten RQA measures were extracted for each recorded scanpath. The measures described the temporal distribution of recurrent fixations as well as laminar and deterministic eye movements. Recurrent fixations are fixations that are located close to a previously fixated point in a scanpath. Deterministic eye movements represent looking back and forth between two locations, while laminar eye movements indicate detailed scanning of an area with consecutive fixations. The RQA metrics along with six conventional eye-tracking parameters were used to construct a classifier for distinguishing experts' scanpaths from those of less-experienced readers. Leave-one-out cross validation was used for evaluating the classifier. For each reader cohort, the ANOVA analysis was done to study the relationship of RQA measures with breast density, case pathology, readers' expertise, and readers' decisions on the case. The proportions of laminar and deterministic movements involved fixations in the location of lesions were also compared for two reader cohorts using two proportion z-tests.
All RQA measures differed significantly between scanpaths of experienced readers and those of less-experienced readers. The classifier achieved an area under the receiver operating characteristic curve of 0.89 (0.87-0.91) for detecting experts' scanpaths. Proportionately more refixations and laminar and deterministic sequences were in the location of the lesion for the experienced cohort compared to the less-experienced cohort (all P-values < 0.001). Eight and four RQA measures differed between normal and cancer cases for the experienced and less experienced readers, respectively. None of metrics differed between fatty and dense breasts for the less experienced readers, while two measures resulted into a significant difference for the experienced readers. For experts, six measures differed significantly between true negatives and false positives and nine were significantly different between true positives and false negatives. For the less-experienced cohort, the corresponding figures were seven and one measures, respectively.
The RQA measures can quantify the differences among experienced and less experienced radiologists. They also capture differences among experts' scanpaths related to case pathology and radiologists' decisions on the case.
本研究旨在提出一种基于递归量化分析(RQA)指标的分类器,用于区分专家扫描路径和经验较少的读者的扫描路径,并使用 RQA 指标探索乳腺扫描路径的时空动态与病例和放射科医生特征之间的关系。
记录 8 名放射科医生(两个队列:4 名经验丰富和 4 名经验较少)阅读 120 张乳腺 X 光片(59 例癌症,61 例正常)时的眼动。为每个记录的扫描路径提取了 10 个 RQA 度量。这些度量描述了复发性注视的时间分布,以及层状和确定性眼动。复发性注视是指位于扫描路径中先前注视点附近的注视。确定性眼动表示在两个位置之间来回看,而层状眼动表示在一个区域内连续注视以进行详细扫描。使用 RQA 指标和六个常规眼动跟踪参数来构建一个分类器,用于区分专家扫描路径和经验较少的读者的扫描路径。使用留一法交叉验证评估分类器。对于每个读者队列,进行 ANOVA 分析以研究 RQA 指标与乳房密度、病例病理学、读者专业知识和读者对病例的决策之间的关系。使用两比例 z 检验比较两个读者队列中涉及病变部位注视的层状和确定性运动的比例。
经验丰富的读者和经验较少的读者的扫描路径之间的所有 RQA 度量均有显著差异。分类器在检测专家扫描路径时的接收者操作特征曲线下面积为 0.89(0.87-0.91)。与经验较少的队列相比,经验丰富的队列中病变部位的注视中包含更多的重新注视和层状和确定性序列(所有 P 值均<0.001)。对于经验丰富和经验较少的读者,分别有 8 个和 4 个 RQA 指标在正常病例和癌症病例之间存在差异。对于经验较少的读者,脂肪和致密乳房之间没有任何指标存在差异,而对于经验丰富的读者,有两个指标存在显著差异。对于专家,在真阴性和假阳性之间有 6 个度量显著不同,在真阳性和假阴性之间有 9 个度量显著不同。对于经验较少的队列,相应的数字分别为 7 和 1 个度量。
RQA 度量可以量化经验丰富和经验较少的放射科医生之间的差异。它们还可以捕捉与病例病理学和放射科医生对病例的决策相关的专家扫描路径之间的差异。