Deep Learning-Based Image Analysis for the Quantification of Tumor-Induced Angiogenesis in the 3D In Vivo Tumor Model—Establishment and Addition to Laser Speckle Contrast Imaging (LSCI)

URN to cite this document:

urn:nbn:de:bvb:355-epub-527567 Copy

DOI to cite this document:

10.5283/epub.52756 Copy

Kuri, Paulina Mena ; Pion, Eric ; Mahl, Lina ; Kainz, Philipp ; Schwarz, Siegfried ; Brochhausen, Christoph ; Aung, Thiha ; Haerteis, Silke

License: Creative Commons Attribution 4.0 PDF - Published Version (1MB)

Date of publication of this fulltext: 11 Aug 2022 10:03

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Details

Item type:	Article
Journal or Publication Title:	Cells
Volume:	11
Number of Issue or Book Chapter:	15
Page Range:	p. 2321
Date:	28 July 2022
Institutions:	Medicine > Lehrstuhl für Pathologie Biology, Preclinical Medicine > Institut für Anatomie > Lehrstuhl für Molekulare und zelluläre Anatomie
Projects:	Deggendorf Institute of Technology
Identification Number:	Value Type 10.3390/cells11152321 DOI
Keywords:	3D in vivo tumor model; chorioallantoic membrane (CAM); angiogenesis; tumor; laser speckle contrast imaging; image analysis software; CAM assay application; artificial intelligence; deep learning; blood circulation
Dewey Decimal Classification:	500 Science > 570 Life sciences 600 Technology > 610 Medical sciences Medicine
Status:	Published
Refereed:	Yes, this version has been refereed
Created at the University of Regensburg:	Yes
Item ID:	52756

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Abstract

Background: Angiogenesis plays an important role in the growth and metastasis of tumors. We established the CAM assay application, an image analysis software of the IKOSA platform by KML Vision, for the quantification of blood vessels with the in ovo chorioallantoic membrane (CAM) model. We added this proprietary deep learning algorithm to the already established laser speckle contrast imaging ...

Abstract

Background:
Angiogenesis plays an important role in the growth and metastasis of tumors. We established the CAM assay application, an image analysis software of the IKOSA platform by KML Vision, for the quantification of blood vessels with the in ovo chorioallantoic membrane (CAM) model. We added this proprietary deep learning algorithm to the already established laser speckle contrast imaging (LSCI).
Methods:
Angiosarcoma cell line tumors were grafted onto the CAM. Angiogenesis was measured at the beginning and at the end of tumor growth with both measurement methods. The CAM assay application was trained to enable the recognition of in ovo CAM vessels. Histological stains of the tissue were performed and gluconate, an anti-angiogenic substance, was applied to the tumors.
Results:
The angiosarcoma cells formed tumors on the CAM that appeared to stay vital and proliferated. An increase in perfusion was observed using both methods. The CAM assay application was successfully established in the in ovo CAM model and anti-angiogenic effects of gluconate were observed.
Conclusions:
The CAM assay application appears to be a useful method for the quantification of angiogenesis in the CAM model and gluconate could be a potential treatment of angiosarcomas. Both aspects should be evaluated in further research.

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Metadata last modified: 05 Dec 2022 15:01

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