Cargando…
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)
(1) 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 adde...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9367525/ https://www.ncbi.nlm.nih.gov/pubmed/35954165 http://dx.doi.org/10.3390/cells11152321 |
_version_ | 1784765833708306432 |
---|---|
author | Kuri, Paulina Mena Pion, Eric Mahl, Lina Kainz, Philipp Schwarz, Siegfried Brochhausen, Christoph Aung, Thiha Haerteis, Silke |
author_facet | Kuri, Paulina Mena Pion, Eric Mahl, Lina Kainz, Philipp Schwarz, Siegfried Brochhausen, Christoph Aung, Thiha Haerteis, Silke |
author_sort | Kuri, Paulina Mena |
collection | PubMed |
description | (1) 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). (2) 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. (3) 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. (4) 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. |
format | Online Article Text |
id | pubmed-9367525 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93675252022-08-12 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) Kuri, Paulina Mena Pion, Eric Mahl, Lina Kainz, Philipp Schwarz, Siegfried Brochhausen, Christoph Aung, Thiha Haerteis, Silke Cells Article (1) 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). (2) 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. (3) 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. (4) 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. MDPI 2022-07-28 /pmc/articles/PMC9367525/ /pubmed/35954165 http://dx.doi.org/10.3390/cells11152321 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kuri, Paulina Mena Pion, Eric Mahl, Lina Kainz, Philipp Schwarz, Siegfried Brochhausen, Christoph Aung, Thiha Haerteis, Silke 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) |
title | 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) |
title_full | 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) |
title_fullStr | 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) |
title_full_unstemmed | 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) |
title_short | 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) |
title_sort | 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) |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9367525/ https://www.ncbi.nlm.nih.gov/pubmed/35954165 http://dx.doi.org/10.3390/cells11152321 |
work_keys_str_mv | AT kuripaulinamena deeplearningbasedimageanalysisforthequantificationoftumorinducedangiogenesisinthe3dinvivotumormodelestablishmentandadditiontolaserspecklecontrastimaginglsci AT pioneric deeplearningbasedimageanalysisforthequantificationoftumorinducedangiogenesisinthe3dinvivotumormodelestablishmentandadditiontolaserspecklecontrastimaginglsci AT mahllina deeplearningbasedimageanalysisforthequantificationoftumorinducedangiogenesisinthe3dinvivotumormodelestablishmentandadditiontolaserspecklecontrastimaginglsci AT kainzphilipp deeplearningbasedimageanalysisforthequantificationoftumorinducedangiogenesisinthe3dinvivotumormodelestablishmentandadditiontolaserspecklecontrastimaginglsci AT schwarzsiegfried deeplearningbasedimageanalysisforthequantificationoftumorinducedangiogenesisinthe3dinvivotumormodelestablishmentandadditiontolaserspecklecontrastimaginglsci AT brochhausenchristoph deeplearningbasedimageanalysisforthequantificationoftumorinducedangiogenesisinthe3dinvivotumormodelestablishmentandadditiontolaserspecklecontrastimaginglsci AT aungthiha deeplearningbasedimageanalysisforthequantificationoftumorinducedangiogenesisinthe3dinvivotumormodelestablishmentandadditiontolaserspecklecontrastimaginglsci AT haerteissilke deeplearningbasedimageanalysisforthequantificationoftumorinducedangiogenesisinthe3dinvivotumormodelestablishmentandadditiontolaserspecklecontrastimaginglsci |