Eintrag weiter verarbeiten
Continuous representation of tumor microvessel density and detection of angiogenic hotspots in histological whole-slide images
Gespeichert in:
| Veröffentlicht in: | OncoTarget 6(2015), 22, Seite 19163-19176 |
|---|---|
| Personen und Körperschaften: | , , , , |
| Titel: | 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 |
| Format: | E-Book-Kapitel |
| Sprache: | Englisch |
| veröffentlicht: |
June 08, 2015
|
| Gesamtaufnahme: |
: OncoTarget, 6(2015), 22, Seite 19163-19176
, volume:6 |
| Quelle: | Verbunddaten SWB Lizenzfreie Online-Ressourcen |
| Zusammenfassung: | 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. |
|---|---|
| Beschreibung: | Gesehen am 23.02.2018 |
| Umfang: | 14 |
| ISSN: |
1949-2553
|
| DOI: | 10.18632/oncotarget.4383 |