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

Hao Lu,Peter J Van Der Spek,Ivo Palli,Ronald R De Krijger,Andrew P Stubbs,Winand Nm Dinjens,Thomas G Papathomas,David Van Zessen

doi:10.1186/s13000-014-0216-6

Hao Lu, Peter J Van Der Spek + Show 6 more

Open Access

https://doi.org/10.1186/s13000-014-0216-6

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Journal: Diagnostic Pathology	Publication Date: Nov 25, 2014
Citations: 43	License type: CC BY 4.0

Affiliation: Erasmus MC, Reinier de Graaf Hospital

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

Highlights

In prognosis and therapeutics of adrenal cortical carcinoma (ACC), the selection of the most active areas in proliferative rate within a slide and objective quantification of immunohistochemical Ki67 Labelling Index (LI) are of critical importance
Automated Selection of Hotspots (ASH) is delivered as a virtual machine which consists of 3 classes: NanoZoomer Digital Pathology Image (NDPI) Segmentation, Adaptive Step Finding and Reporting Visualization (Figure 1)
Automated selection of hotspots The overall work flow for the image analysis outlined in Figure 3 includes the main classes developed for ASH which include NPI Segmentation, Adaptive Step Finding and Visual Reporting

Summary

Introduction

In 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. According to recent evidence from the European Network for the Study of Adrenal Tumors (ENS@T) ACC study group, the resection status and the Ki67 labelling index (LI) in both localized and advanced ACC [2,3] constitute the most relevant prognostic parameters [4]. In this regard, it has been suggested that the histopathology report should include Ki67 LI along with confirmation of the adrenocortical origin on immunohistochemical grounds, Weiss score and resection status [4]. As far as ACCs are concerned, there is lack of studies addressing the issues of a potential biological heterogeneity of Ki67 staining and interobserver variation, and different methods of objective quantification of the Ki67 proliferative index

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