Research Papers:

Continuous representation of tumor microvessel density and detection of angiogenic hotspots in histological whole-slide images

Jakob Nikolas Kather, Alexander Marx, Constantino Carlos Reyes-Aldasoro, Lothar R. Schad, Frank Gerrit Zöllner and Cleo-Aron Weis _

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Oncotarget. 2015; 6:19163-19176. https://doi.org/10.18632/oncotarget.4383

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Jakob Nikolas Kather1,2, Alexander Marx1, Constantino Carlos Reyes-Aldasoro3, Lothar R. Schad2, Frank Gerrit Zöllner2 and Cleo-Aron Weis1

1 Institute of Pathology, University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany

2 Computer Assisted Clinical Medicine, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany

3 School of Engineering and Mathematical Sciences, City University London EC1V OHB, United Kingdom

Correspondence to:

Cleo-Aron Weis, email:

Keywords: tumor angiogenesis, digital pathology, spatial statistics, vessel density

Received: March 21, 2015 Accepted: April 08, 2015 Published: June 08, 2015


Blood vessels in solid tumors are not randomly distributed, but are clustered in angiogenic hotspots. Tumor microvessel density (MVD) within these hotspots correlates with patient survival and is widely used both in diagnostic routine and in clinical trials. Still, these hotspots are usually subjectively defined. There is no unbiased, continuous and explicit representation of tumor vessel distribution in histological whole slide images. This shortcoming distorts angiogenesis measurements and may account for ambiguous results in the literature.

In the present study, we describe and evaluate a new method that eliminates this bias and makes angiogenesis quantification more objective and more efficient. Our approach involves automatic slide scanning, automatic image analysis and spatial statistical analysis. By comparing a continuous MVD function of the actual sample to random point patterns, we introduce an objective criterion for hotspot detection: An angiogenic hotspot is defined as a clustering of blood vessels that is very unlikely to occur randomly. We evaluate the proposed method in N=11 images of human colorectal carcinoma samples and compare the results to a blinded human observer. For the first time, we demonstrate the existence of statistically significant hotspots in tumor images and provide a tool to accurately detect these hotspots.

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