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

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.

Tissue heterogeneity contributes to suboptimal precision of WHO 2010 scoring criteria for Ki67 labeling index in a subset of neuroendocrine neoplasms of the pancreascriteria for Ki67 labeling index in a subset of neuroendocrine neoplasms of the pancreas

“…Important aspect of Ki67 scoring is selection of tumour regions for counting [42,53,73,74,75,76,77,78]. Current counting methodology in NEN is based on recognition of HS.…”

“…This comes from assumption that results obtained in HS rather than in CS or in "average" areas are critical for prognosis [59]. HS is somewhat poorly defined term [74,78]. It is usually defined as a high-proliferation-area [78].…”

“…It is usually defined as a high-proliferation-area [78]. HS may differ in size, shape and gradient of stain from HS to surrounding areas [42,74,78]. Density of cells in particular tumor area may also influence selection of this area as HS [75].…”

“…Density of cells in particular tumor area may also influence selection of this area as HS [75]. HS selection is done subjectively and results of these choices (at least in breast cancer) do not necessarily correlated with HS recognized using digital image analysis [73,74]. Automated HS detection may be an alternative [74,76,77,79,80].…”

“…HS selection is done subjectively and results of these choices (at least in breast cancer) do not necessarily correlated with HS recognized using digital image analysis [73,74]. Automated HS detection may be an alternative [74,76,77,79,80]. It is not fully clear whether single HS is enough for Ki67 LI assessment or whether several HS should be counted [81].…”