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Investigation of artificial intelligence integrated fluorescence endoscopy image analysis with indocyanine green for interpretation of precancerous lesions in colon cancer
Indocyanine green (ICG) has been used in clinical practice for more than 40 years and its safety and preferential accumulation in tumors has been reported for various tumor types, including colon cancer. However, reports on clinical assessments of ICG-based molecular endoscopy imaging for precancero...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10212120/ https://www.ncbi.nlm.nih.gov/pubmed/37228164 http://dx.doi.org/10.1371/journal.pone.0286189 |
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author | Kim, Jinhyeon Kim, Hajung Yoon, Yong Sik Kim, Chan Wook Hong, Seung-Mo Kim, Sungjee Choi, Doowon Chun, Jihyun Hong, Seung Wook Hwang, Sung Wook Park, Sang Hyoung Yang, Dong-Hoon Ye, Byong Duk Byeon, Jeong-Sik Yang, Suk-Kyun Kim, Sun Young Myung, Seung-Jae |
author_facet | Kim, Jinhyeon Kim, Hajung Yoon, Yong Sik Kim, Chan Wook Hong, Seung-Mo Kim, Sungjee Choi, Doowon Chun, Jihyun Hong, Seung Wook Hwang, Sung Wook Park, Sang Hyoung Yang, Dong-Hoon Ye, Byong Duk Byeon, Jeong-Sik Yang, Suk-Kyun Kim, Sun Young Myung, Seung-Jae |
author_sort | Kim, Jinhyeon |
collection | PubMed |
description | Indocyanine green (ICG) has been used in clinical practice for more than 40 years and its safety and preferential accumulation in tumors has been reported for various tumor types, including colon cancer. However, reports on clinical assessments of ICG-based molecular endoscopy imaging for precancerous lesions are scarce. We determined visualization ability of ICG fluorescence endoscopy in colitis-associated colon cancer using 30 lesions from an azoxymethane/dextran sulfate sodium (AOM/DSS) mouse model and 16 colon cancer patient tissue-samples. With a total of 60 images (optical, fluorescence) obtained during endoscopy observation of mouse colon cancer, we used deep learning network to predict four classes (Normal, Dysplasia, Adenoma, and Carcinoma) of colorectal cancer development. ICG could detect 100% of carcinoma, 90% of adenoma, and 57% of dysplasia, with little background signal at 30 min after injection via real-time fluorescence endoscopy. Correlation analysis with immunohistochemistry revealed a positive correlation of ICG with inducible nitric oxide synthase (iNOS; r > 0.5). Increased expression of iNOS resulted in increased levels of cellular nitric oxide in cancer cells compared to that in normal cells, which was related to the inhibition of drug efflux via the ABCB1 transporter down-regulation resulting in delayed retention of intracellular ICG. With artificial intelligence training, the accuracy of image classification into four classes using data sets, such as fluorescence, optical, and fluorescence/optical images was assessed. Fluorescence images obtained the highest accuracy (AUC of 0.8125) than optical and fluorescence/optical images (AUC of 0.75 and 0.6667, respectively). These findings highlight the clinical feasibility of ICG as a detector of precancerous lesions in real-time fluorescence endoscopy with artificial intelligence training and suggest that the mechanism of ICG retention in cancer cells is related to intracellular nitric oxide concentration. |
format | Online Article Text |
id | pubmed-10212120 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-102121202023-05-26 Investigation of artificial intelligence integrated fluorescence endoscopy image analysis with indocyanine green for interpretation of precancerous lesions in colon cancer Kim, Jinhyeon Kim, Hajung Yoon, Yong Sik Kim, Chan Wook Hong, Seung-Mo Kim, Sungjee Choi, Doowon Chun, Jihyun Hong, Seung Wook Hwang, Sung Wook Park, Sang Hyoung Yang, Dong-Hoon Ye, Byong Duk Byeon, Jeong-Sik Yang, Suk-Kyun Kim, Sun Young Myung, Seung-Jae PLoS One Research Article Indocyanine green (ICG) has been used in clinical practice for more than 40 years and its safety and preferential accumulation in tumors has been reported for various tumor types, including colon cancer. However, reports on clinical assessments of ICG-based molecular endoscopy imaging for precancerous lesions are scarce. We determined visualization ability of ICG fluorescence endoscopy in colitis-associated colon cancer using 30 lesions from an azoxymethane/dextran sulfate sodium (AOM/DSS) mouse model and 16 colon cancer patient tissue-samples. With a total of 60 images (optical, fluorescence) obtained during endoscopy observation of mouse colon cancer, we used deep learning network to predict four classes (Normal, Dysplasia, Adenoma, and Carcinoma) of colorectal cancer development. ICG could detect 100% of carcinoma, 90% of adenoma, and 57% of dysplasia, with little background signal at 30 min after injection via real-time fluorescence endoscopy. Correlation analysis with immunohistochemistry revealed a positive correlation of ICG with inducible nitric oxide synthase (iNOS; r > 0.5). Increased expression of iNOS resulted in increased levels of cellular nitric oxide in cancer cells compared to that in normal cells, which was related to the inhibition of drug efflux via the ABCB1 transporter down-regulation resulting in delayed retention of intracellular ICG. With artificial intelligence training, the accuracy of image classification into four classes using data sets, such as fluorescence, optical, and fluorescence/optical images was assessed. Fluorescence images obtained the highest accuracy (AUC of 0.8125) than optical and fluorescence/optical images (AUC of 0.75 and 0.6667, respectively). These findings highlight the clinical feasibility of ICG as a detector of precancerous lesions in real-time fluorescence endoscopy with artificial intelligence training and suggest that the mechanism of ICG retention in cancer cells is related to intracellular nitric oxide concentration. Public Library of Science 2023-05-25 /pmc/articles/PMC10212120/ /pubmed/37228164 http://dx.doi.org/10.1371/journal.pone.0286189 Text en © 2023 Kim et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Kim, Jinhyeon Kim, Hajung Yoon, Yong Sik Kim, Chan Wook Hong, Seung-Mo Kim, Sungjee Choi, Doowon Chun, Jihyun Hong, Seung Wook Hwang, Sung Wook Park, Sang Hyoung Yang, Dong-Hoon Ye, Byong Duk Byeon, Jeong-Sik Yang, Suk-Kyun Kim, Sun Young Myung, Seung-Jae Investigation of artificial intelligence integrated fluorescence endoscopy image analysis with indocyanine green for interpretation of precancerous lesions in colon cancer |
title | Investigation of artificial intelligence integrated fluorescence endoscopy image analysis with indocyanine green for interpretation of precancerous lesions in colon cancer |
title_full | Investigation of artificial intelligence integrated fluorescence endoscopy image analysis with indocyanine green for interpretation of precancerous lesions in colon cancer |
title_fullStr | Investigation of artificial intelligence integrated fluorescence endoscopy image analysis with indocyanine green for interpretation of precancerous lesions in colon cancer |
title_full_unstemmed | Investigation of artificial intelligence integrated fluorescence endoscopy image analysis with indocyanine green for interpretation of precancerous lesions in colon cancer |
title_short | Investigation of artificial intelligence integrated fluorescence endoscopy image analysis with indocyanine green for interpretation of precancerous lesions in colon cancer |
title_sort | investigation of artificial intelligence integrated fluorescence endoscopy image analysis with indocyanine green for interpretation of precancerous lesions in colon cancer |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10212120/ https://www.ncbi.nlm.nih.gov/pubmed/37228164 http://dx.doi.org/10.1371/journal.pone.0286189 |
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