Investigation of artificial intelligence integrated fluorescence endoscopy image analysis with indocyanine green for interpretation of precancerous lesions in colon cancer

被引:4
作者
Kim, Jinhyeon [1 ]
Kim, Hajung [2 ]
Yoon, Yong Sik [3 ]
Kim, Chan Wook [4 ]
Hong, Seung-Mo [1 ,5 ]
Kim, Sungjee [6 ,7 ]
Choi, Doowon [7 ]
Chun, Jihyun [5 ]
Hong, Seung Wook [1 ,3 ]
Hwang, Sung Wook [1 ,3 ]
Park, Sang Hyoung [3 ]
Yang, Dong-Hoon [3 ]
Ye, Byong Duk [1 ,3 ]
Byeon, Jeong-Sik [3 ]
Yang, Suk-Kyun [3 ]
Kim, Sun Young [8 ]
Myung, Seung-Jae [1 ,3 ,9 ]
机构
[1] Univ Ulsan, Coll Med, Digest Dis Res Ctr, Seoul, South Korea
[2] Asan Med Ctr, Convergence Med Res Ctr, Seoul, South Korea
[3] Univ Ulsan, Coll Med, Asan Med Ctr, Dept Gastroenterol, Seoul, South Korea
[4] Univ Ulsan, Coll Med, Asan Med Ctr, Dept Colon & Rectal Surg, Seoul, South Korea
[5] Univ Ulsan, Coll Med, Asan Med Ctr, Dept Pathol, Seoul, South Korea
[6] Pohang Univ Sci & Technol, Dept Chem, Pohang, Gyeongbuk, South Korea
[7] Pohang Univ Sci & Technol, Sch Interdisciplinary Biosci & Bioengn, Pohang, Gyeongbuk, South Korea
[8] Univ Ulsan, Asan Inst Life Sci, Coll Med, Asan Med Ctr, Seoul, South Korea
[9] Edis Biotech, Seoul, South Korea
基金
新加坡国家研究基金会;
关键词
MACROMOLECULAR DRUG-DELIVERY; GUIDED SURGERY; COLORECTAL-CANCER; INFLAMMATION; RESISTANCE; CARCINOMA; TOXICITY; POLYPS; DETECT; TIME;
D O I
10.1371/journal.pone.0286189
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:17
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