Clustering methods applied in the detection of Ki67 hot‐spots in whole tumor slide images: An efficient way to characterize heterogeneous tissue‐based biomarkers

Abstract

Whole-slide scanners allow the digitization of an entire histological slide at very high resolution. This new acquisition technique opens a wide range of possibilities for addressing challenging image analysis problems, including the identification of tissuebased biomarkers. In this study, we use whole-slide scanner technology for imaging the proliferating activity patterns in tumor slides based on Ki67 immunohistochemistry. Faced with large images, pathologists require tools that can help them identify tumor regions that exhibit high proliferating activity, called ''hot-spots'' (HSs). Pathologists need tools that can quantitatively characterize these HS patterns. To respond to this clinical need, the present study investigates various clustering methods with the aim of identifying Ki67 HSs in whole tumor slide images. This task requires a method capable of identifying an unknown number of clusters, which may be highly variable in terms of shape, size, and density. We developed a hybrid clustering method, referred to as Seedlink. Compared to manual HS selections by three pathologists, we show that Seedlink provides an efficient way of detecting Ki67 HSs and improves the agreement among pathologists when identifying HSs. '

Consensus guidelines for the use and interpretation of angiogenesis assays

“…The Blood Vessel Analysis algorithm of Definiens™ segments and classifies blood vessel objects (orange) and nuclei (blue) (mid). The Cartesian coordinates ( x , y ) of the centroids of all vessel objects in one ROI were used in a simplified “SeedLink” clustering method [646] (right). Centroids with the same color (e.g., red) belong to the same cluster.…”

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.…”

“…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].…”

Perceptual clustering for automatic hotspot detection from Ki‐67‐stained neuroendocrine tumour images

“…In Lopez et al . (), a slightly modified version of DBSCAN, namely Seedlink, was presented to detect hotspots in high‐grade gliomas. It first determines the preliminary set of hotspots by finding the valley in the sorted distance‐based distribution.…”