Journal of Pathology Informatics Journal of Pathology Informatics
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RESEARCH ARTICLE
Year : 2019  |  Volume : 10  |  Issue : 1  |  Page : 8

Ki67 quantitative interpretation: Insights using image analysis


1 Department of Pathology, Laboratory Medicine Program, University Health Network; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
2 Department of Pathology, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia

Correspondence Address:
Dr. Sylvia L Asa
200 Elizabeth Street, 11th Floor, Toronto, Ontario M5G 2M9
Canada
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jpi.jpi_76_18

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Background: Proliferation markers, especially Ki67, are increasingly important in diagnosis and prognosis. The best method for calculating Ki67 is still the subject of debate. Materials and Methods: 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. Results: 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. Conclusions: 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.


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