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A methodology for comprehensive breast cancer Ki67 labeling index with intra-tumor heterogeneity appraisal based on hexagonal tiling of digital image analysis data

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Abstract

Digital image analysis (DIA) enables higher accuracy, reproducibility, and capacity to enumerate cell populations by immunohistochemistry; however, the most unique benefits may be obtained by evaluating the spatial distribution and intra-tissue variance of markers. The proliferative activity of breast cancer tissue, estimated by the Ki67 labeling index (Ki67 LI), is a prognostic and predictive biomarker requiring robust measurement methodologies. We performed DIA on whole-slide images (WSI) of 302 surgically removed Ki67-stained breast cancer specimens; the tumour classifier algorithm was used to automatically detect tumour tissue but was not trained to distinguish between invasive and non-invasive carcinoma cells. The WSI DIA-generated data were subsampled by hexagonal tiling (HexT). Distribution and texture parameters were compared to conventional WSI DIA and pathology report data. Factor analysis of the data set, including total numbers of tumor cells, the Ki67 LI and Ki67 distribution, and texture indicators, extracted 4 factors, identified as entropy, proliferation, bimodality, and cellularity. The factor scores were further utilized in cluster analysis, outlining subcategories of heterogeneous tumors with predominant entropy, bimodality, or both at different levels of proliferative activity. The methodology also allowed the visualization of Ki67 LI heterogeneity in tumors and the automated detection and quantitative evaluation of Ki67 hotspots, based on the upper quintile of the HexT data, conceptualized as the “Pareto hotspot”. We conclude that systematic subsampling of DIA-generated data into HexT enables comprehensive Ki67 LI analysis that reflects aspects of intra-tumor heterogeneity and may serve as a methodology to improve digital immunohistochemistry in general.

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Acknowledgments

This research is funded by the European Social Fund under the Global Grant measure, Grant #VP1-3.1-SMM-07-K-03-051.

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Authors and Affiliations

  1. Path-Image/BioTiCla, University Caen Normandy, Caen, France

    Benoit Plancoulaine

  2. Department of Pathology, Forensic Medicine and Pharmacology, Faculty of Medicine, Vilnius University, Vilnius, Lithuania

    Aida Laurinaviciene, Paulette Herlin, Justinas Besusparis, Raimundas Meskauskas, Indra Baltrusaityte, Yasir Iqbal & Arvydas Laurinavicius

  3. National Center of Pathology, Affiliate of Vilnius University Hospital Santariskiu Clinics, P. Baublio 5, 08406, Vilnius, Lithuania

    Aida Laurinaviciene, Justinas Besusparis, Raimundas Meskauskas, Indra Baltrusaityte & Arvydas Laurinavicius

Authors
  1. Benoit Plancoulaine
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  2. Aida Laurinaviciene
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  3. Paulette Herlin
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  4. Justinas Besusparis
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  8. Arvydas Laurinavicius
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Correspondence to Arvydas Laurinavicius.

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Plancoulaine, B., Laurinaviciene, A., Herlin, P. et al. A methodology for comprehensive breast cancer Ki67 labeling index with intra-tumor heterogeneity appraisal based on hexagonal tiling of digital image analysis data. Virchows Arch 467, 711–722 (2015). https://doi.org/10.1007/s00428-015-1865-x

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