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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
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.
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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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