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Proliferation assessment in breast carcinomas using digital image analysis based on virtual Ki67/cytokeratin double staining



Proliferation assessment in breast carcinomas using digital image analysis based on virtual Ki67/cytokeratin double staining



Breast Cancer Research and Treatment 158(1): 11-19



Manual estimation of Ki67 Proliferation Index (PI) in breast carcinoma classification is labor intensive and prone to intra- and interobserver variation. Standard Digital Image Analysis (DIA) has limitations due to issues with tumor cell identification. Recently, a computer algorithm, DIA based on Virtual Double Staining (VDS), segmenting Ki67-positive and -negative tumor cells using digitally fused parallel cytokeratin (CK) and Ki67-stained slides has been introduced. In this study, we compare VDS with manual stereological counting of Ki67-positive and -negative cells and examine the impact of the physical distance of the parallel slides on the alignment of slides. TMAs, containing 140 cores of consecutively obtained breast carcinomas, were stained for CK and Ki67 using optimized staining protocols. By means of stereological principles, Ki67-positive and -negative cell profiles were counted in sampled areas and used for the estimation of PIs of the whole tissue core. The VDS principle was applied to both the same sampled areas and the whole tissue core. Additionally, five neighboring slides were stained for CK in order to examine the alignment algorithm. Correlation between manual counting and VDS in both sampled areas and whole core was almost perfect (correlation coefficients above 0.97). Bland-Altman plots did not reveal any skewness in any data ranges. There was a good agreement in alignment (>85 %) in neighboring slides, whereas agreement decreased in non-neighboring slides. VDS gave similar results compared with manual counting using stereological principles. Introduction of this method in clinical and research practice may improve accuracy and reproducibility of Ki67 PI.

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Accession: 058641006

Download citation: RISBibTeXText

PMID: 27283833

DOI: 10.1007/s10549-016-3852-6


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