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Real-time automated diagnosis of colorectal cancer invasion depth using a deep learning model with multimodal data (with video).

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收录情况： ◇ SCIE

作者：
Lu Zihua[1,2,3] * ; Xu Youming[1,2,3] * ; Yao Liwen[1,2,3] * ; Zhou Wei[1,2,3] ; Gong Wei[4] ; Yang Genhua[4] ; Guo Mingwen[5] ; Zhang Beiping[6] ; Huang Xu[1,2,3] ; He Chunping[1,2,3] ; Zhou Rui[1,2,3] ; Deng Yunchao[1,2,3] ; Yu Honggang[1,2,3,*1] ;
机构： [1]Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China [2]Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China [3]Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China [4]Department of Gastroenterology, Shenzhen Hospital of Southern Medical University, Shenzhen, China [5]Department of Gastroenterology, The First Hospital of Yichang, Yichang, China [6]Department of Gastroenterology, Guangdong Province Traditional Chinese Medical Hospital, Guangzhou, China
出处：
DOI： 10.1016/j.gie.2021.11.049
ISSN：

摘要：
The optical diagnosis of colorectal cancer (CRC) invasion depth with white light (WL) and image enhanced endoscopy (IEE) remains challenging. We aimed to construct and validate a 2-modal deep learning based system, incorporated with both WL and IEE images (named Endo-CRC) in estimating the invasion depth of CRC.Samples were retrospectively obtained from 3 hospitals in China. We combined WL and IEE images into image-pairs. Altogether, 337278 image-pairs from 268 noninvasive and superficial CRC and 181934 image-pairs from 82 deep CRC were used for training. A total of 296644 and 4528 image-pairs were used for internal and external tests, and for comparison with endoscopists. 35 videos were used for evaluating the real-time performance of the Endo-CRC system. Two deep learning models, solely using WL (model W) and IEE images (model I) were constructed to compare with Endo-CRC.The accuracy of Endo-CRC in internal image test with and without advanced CRC were 91.61% and 93.78%, respectively, and 88.65% in external test, which did not include advanced CRC. In man-machine competition, Endo-CRC achieved an expert comparable accuracy of 88.11%, and achieved the highest sensitivity compared with all endoscopists. In video test, Endo-CRC achieved an accuracy of 100.00%. Compared with model W and model I, Endo-CRC performed higher accuracy (91.61% versus 88.27% compared with model I; 91.61% versus 81.32% compared with model W; per image-pair).The Endo-CRC system has great potential for assisting in CRC invasion depth diagnosis and may be well applied in clinical practice.Copyright © 2021 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.

基金：
Project of Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision (grant no. 2018BCC337); Hubei Province Major Science and Technology Innovation Project (grant no. 2018-916-000-008). Independent Research Project of Wuhan University (grant no. 2042021kf0084).
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外文
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中科院(CAS)分区：
出版当年[2021]版：
大类 | 1 区 医学
小类 | 2 区 胃肠肝病学
最新[2025]版：
大类 | 1 区 医学
小类 | 2 区 胃肠肝病学
JCR分区：
出版当年[2020]版：
Q1 GASTROENTEROLOGY & HEPATOLOGY
最新[2023]版：
Q1 GASTROENTEROLOGY & HEPATOLOGY

影响因子： 最新[2023版] 最新五年平均 出版当年[2020版] 出版当年五年平均 出版前一年[2019版] 出版后一年[2021版]

第一作者：
第一作者机构： [1]Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China [2]Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China [3]Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
共同第一作者：
通讯作者：
通讯机构： [1]Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China [2]Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China [3]Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China [*1]Department of Gastroenterology, Renmin Hospital of Wuhan University. Hubei Province, China.
推荐引用方式(GB/T 7714)：
Lu Zihua,Xu Youming,Yao Liwen,et al.Real-time automated diagnosis of colorectal cancer invasion depth using a deep learning model with multimodal data (with video).[J].GASTROINTESTINAL ENDOSCOPY.2022,95(6):1186-+.doi:10.1016/j.gie.2021.11.049.
APA：
Lu Zihua,Xu Youming,Yao Liwen,Zhou Wei,Gong Wei...&Yu Honggang.(2022).Real-time automated diagnosis of colorectal cancer invasion depth using a deep learning model with multimodal data (with video)..GASTROINTESTINAL ENDOSCOPY,95,(6)
MLA：
Lu Zihua,et al."Real-time automated diagnosis of colorectal cancer invasion depth using a deep learning model with multimodal data (with video).".GASTROINTESTINAL ENDOSCOPY 95..6(2022):1186-+

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