Ki67 Quantitative Interpretation: Insights using Image Analysis

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.

[1]  D. Louis,et al.  Proliferating cell nuclear antigen and Ki-67 immunohistochemistry in brain tumors: A comparative study , 2004, Acta Neuropathologica.

[2]  Laura H. Tang,et al.  Effect of Tumor Heterogeneity on the Assessment of Ki67 Labeling Index in Well-differentiated Neuroendocrine Tumors Metastatic to the Liver: Implications for Prognostic Stratification , 2011, The American journal of surgical pathology.

[3]  D. Zelterman,et al.  Downregulation of p27KIP1 and Ki67/Mib1 labeling index support the classification of thyroid carcinoma into prognostically relevant categories. , 1999, The American journal of surgical pathology.

[4]  Eberhard Korsching,et al.  Interlaboratory variability of Ki67 staining in breast cancer. , 2017, European Journal of Cancer.

[5]  N. Carr,et al.  Accuracy of visual assessments of proliferation indices in gastroenteropancreatic neuroendocrine tumours , 2013, Journal of Clinical Pathology.

[6]  Andrew P Stubbs,et al.  An International Ki67 Reproducibility Study in Adrenal Cortical Carcinoma , 2016, The American journal of surgical pathology.

[7]  Toby C. Cornish,et al.  Grading of Well-differentiated Pancreatic Neuroendocrine Tumors Is Improved by the Inclusion of Both Ki67 Proliferative Index and Mitotic Rate , 2013, The American journal of surgical pathology.

[8]  John M S Bartlett,et al.  An international study to increase concordance in Ki67 scoring , 2015, Modern Pathology.

[9]  Michael Goodman,et al.  Calculation of the Ki67 index in pancreatic neuroendocrine tumors: a comparative analysis of four counting methodologies , 2015, Modern Pathology.

[10]  Alexis B. Carter,et al.  Validating whole slide imaging for diagnostic purposes in pathology: guideline from the College of American Pathologists Pathology and Laboratory Quality Center. , 2013, Archives of pathology & laboratory medicine.

[11]  C. Rowsell,et al.  Variability of Ki67 labeling index in multiple neuroendocrine tumors specimens over the course of the disease. , 2014, European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology.

[12]  Zoya I. Volynskaya,et al.  Integrated Pathology Informatics Enables High-Quality Personalized and Precision Medicine: Digital Pathology and Beyond. , 2017, Archives of pathology & laboratory medicine.

[13]  F. Bergmann,et al.  Interlaboratory variability of MIB1 staining in well-differentiated pancreatic neuroendocrine tumors , 2015, Virchows Archiv.

[14]  Laura H. Tang,et al.  Objective Quantification of the Ki67 Proliferative Index in Neuroendocrine Tumors of the Gastroenteropancreatic System: A Comparison of Digital Image Analysis With Manual Methods , 2012, The American journal of surgical pathology.

[15]  D Schmidt,et al.  Correlation between mitotic and Ki-67 labeling indices in paraffin-embedded carcinoma specimens. , 1998, Human pathology.

[16]  Johannes Gerdes,et al.  Production of a mouse monoclonal antibody reactive with a human nuclear antigen associated with cell proliferation , 1983, International journal of cancer.