Kuri, Paulina Mena and Pion, Eric and Mahl, Lina and Kainz, Philipp and Schwarz, Siegfried and Brochhausen, Christoph and Aung, Thiha and Haerteis, Silke (2022) 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). CELLS, 11 (15): 2321. ISSN , 2073-4409
Full text not available from this repository. (Request a copy)Abstract
(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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | CHICK CHORIOALLANTOIC MEMBRANE; CAM ASSAY; CANCER; ANGIOSARCOMA; CELLS; EXPRESSION; INVASION; OUTCOMES; GROWTH; 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 |
| Subjects: | 500 Science > 570 Life sciences 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Pathologie Biology, Preclinical Medicine > Institut für Anatomie > Lehrstuhl für Molekulare und zelluläre Anatomie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 12 Dec 2023 08:27 |
| Last Modified: | 12 Dec 2023 08:27 |
| URI: | https://pred.uni-regensburg.de/id/eprint/56992 |
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