Automated Selection of Hotspots (ASH): enhanced automated segmentation and adaptive step finding for Ki67 hotspot detection in adrenal cortical cancer

Abstract

BackgroundIn prognosis and therapeutics of adrenal cortical carcinoma (ACC), the selection of the most active areas in proliferative rate (hotspots) within a slide and objective quantification of immunohistochemical Ki67 Labelling Index (LI) are of critical importance. In addition to intratumoral heterogeneity in proliferative rate i.e. levels of Ki67 expression within a given ACC, lack of uniformity and reproducibility in the method of quantification of Ki67 LI may confound an accurate assessment of Ki67 LI.ResultsWe have implemented an open source toolset, Automated Selection of Hotspots (ASH), for automated hotspot detection and quantification of Ki67 LI. ASH utilizes NanoZoomer Digital Pathology Image (NDPI) splitter to convert the specific NDPI format digital slide scanned from the Hamamatsu instrument into a conventional tiff or jpeg format image for automated segmentation and adaptive step finding hotspots detection algorithm. Quantitative hotspot ranking is provided by the functionality from the open source application ImmunoRatio as part of the ASH protocol. The output is a ranked set of hotspots with concomitant quantitative values based on whole slide ranking.ConclusionWe have implemented an open source automated detection quantitative ranking of hotspots to support histopathologists in selecting the ‘hottest’ hotspot areas in adrenocortical carcinoma. To provide wider community easy access to ASH we implemented a Galaxy virtual machine (VM) of ASH which is available from http://bioinformatics.erasmusmc.nl/wiki/Automated_Selection_of_Hotspots.Virtual SlidesThe virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/13000_2014_216

Spatial Heterogeneity in the Tumor Microenvironment

“…With advancing computing techniques, remarkable progress in image analysis has been made on objective assessment of cellular context in digitized cancer histological sections. The use of machine learning methods enables automated identification of various cell types, tumor components, and regions based on human expert input, namely, supervised learning (Holmes et al 2009;Basavanhally et al 2010;Tuominen et al 2010;Balsat et al 2011Balsat et al , 2014Beck et al 2011;Doyle et al 2012;Yuan et al 2012;Lu et al 2014). The computer compares a new cell with what human experts call a cancer, stromal, or other cell types and determines its type based on morphological similarity (Fig.…”

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

“…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].…”

Dissecting morphological and molecular heterogeneity in adrenocortical carcinoma

“…Moreover, several groups proved that Ki-67 is superior to mitotic count to prognosticate aCC, although the optimal cut offs are still to be identified among those sp far proposed -by Duregon et al (26) (<20%, 20-50% and >50%) and by beuschlein et al (27) (<10%, 10-20%)-. in addition, as for several other cancer types, the mitotic/ proliferative activity within individual lesions is frequently heterogeneous (Figure 3) and the evaluation of hot-spot areas is preferable, as also recently suggested (28).…”