Volynskaya Zoya, Mete Ozgur, Pakbaz Sara, Al-Ghamdi Doaa, Asa Sylvia L
Department of Pathology, Laboratory Medicine Program, University Health Network, Toronto, Ontario, Canada.
Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.
J Pathol Inform. 2019 Mar 8;10:8. doi: 10.4103/jpi.jpi_76_18. eCollection 2019.
Proliferation markers, especially Ki67, are increasingly important in diagnosis and prognosis. The best method for calculating Ki67 is still the subject of debate.
We evaluated an image analysis tool for quantitative interpretation of Ki67 in neuroendocrine tumors and compared it to manual counts. We expanded a primary digital pathology platform to include the Leica Biosystems image analysis nuclear algorithm. Slides were digitized using a Leica Aperio AT2 Scanner and accessed through the Cerner CoPath LIS interfaced with Aperio eSlideManager through Aperio ImageScope. Selected regions of interest (ROIs) were manually defined and annotated to include tumor cells only; they were then analyzed with the algorithm and by four pathologists counting on printed images. After validation, the algorithm was used to examine the impact of the size and number of areas selected as ROIs.
The algorithm provided reproducible results that were obtained within seconds, compared to up to 55 min of manual counting that varied between users. Benefits of image analysis identified by users included accuracy, time savings, and ease of viewing. Access to the algorithm allowed rapid comparisons of Ki67 counts in ROIs that varied in numbers of cells and selection of fields, the outputs demonstrated that the results vary around defined cutoffs that provide tumor grade depending on the number of cells and ROIs counted.
Digital image analysis provides accurate and reproducible quantitative data faster than manual counts. However, access to this tool allows multiple analyses of a single sample to use variable numbers of cells and selection of variable ROIs that can alter the result in clinically significant ways. This study highlights the potential risk of hard cutoffs of continuous variables and indicates that standardization of number of cells and number of regions selected for analysis should be incorporated into guidelines for Ki67 calculations.
增殖标志物，尤其是Ki67，在诊断和预后中越来越重要。计算Ki67的最佳方法仍是争论的焦点。
我们评估了一种用于神经内分泌肿瘤中Ki67定量解读的图像分析工具，并将其与人工计数进行比较。我们扩展了一个主要的数字病理学平台，使其包含徕卡生物系统图像分析核算法。使用徕卡Aperio AT2扫描仪对玻片进行数字化处理，并通过与Aperio eSlideManager接口的Cerner CoPath LIS，经由Aperio ImageScope进行访问。手动定义并标注选定的感兴趣区域（ROI），使其仅包括肿瘤细胞；然后使用该算法以及由四位病理学家对打印图像进行计数来进行分析。验证后，使用该算法检查所选ROI区域的大小和数量的影响。
该算法提供了可重复的结果，在数秒内即可获得，相比之下，人工计数则需要长达55分钟，且不同用户之间存在差异。用户确定的图像分析的优点包括准确性、节省时间和易于查看。使用该算法可以快速比较细胞数量和视野选择不同的ROI中的Ki67计数，结果表明，根据计数的细胞数量和ROI，结果围绕确定的用于提供肿瘤分级的临界值而变化。
数字图像分析比人工计数能更快地提供准确且可重复的定量数据。然而，使用该工具可对单个样本进行多次分析，以使用不同数量的细胞并选择不同的ROI，这可能会以具有临床意义的方式改变结果。本研究强调了对连续变量进行硬性临界值划分的潜在风险，并表明在Ki67计算指南中应纳入分析所选细胞数量和区域数量的标准化内容。
