Investigation of artificial intelligence integrated fluorescence endoscopy image analysis with indocyanine green for interpretation of precancerous lesions in colon cancer
- Abstract
- 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.
- Issued Date
- 2023
Jinhyeon Kim
Hajung Kim
Yong Sik Yoon
Chan Wook Kim
Seung-Mo Hong
Sungjee Kim
Doowon Choi
Jihyun Chun
Seung Wook Hong
Sung Wook Hwang
Sang Hyoung Park
Dong-Hoon Yang
Byong Duk Ye
Jeong-Sik Byeon
Suk-Kyun Yang
Sun Young Kim
Seung-Jae Myung
- Type
- Article
- Keyword
- Research and analysis methods; Imaging techniques; Fluorescence imaging; Medicine and health sciences; Oncology; Cancers and neoplasms; Colorectal cancer; Surgical and invasive medical procedures; Endoscopy; Malignant tumors; Biology and life sciences; Biochemistry; Neurochemistry; Neurochemicals; Nitric oxide; Neuroscience; Clinical medicine; Signs and symptoms; Lesions; Adenomas; Animal studies; Experimental organism systems; Model organisms; Mouse models; Animal models
- DOI
- 10.1371/journal.pone.0286189
- URI
- https://oak.ulsan.ac.kr/handle/2021.oak/15943
- Publisher
- PLoS One
- Language
- 한국어
- ISSN
- 1932-6203
- Citation Volume
- 18
- Citation Number
- 5
- Citation Start Page
- 1
- Citation End Page
- 17
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- Medicine > Nursing
- 공개 및 라이선스
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