Observational Study
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jan 21, 2021; 27(3): 281-293
Published online Jan 21, 2021. doi: 10.3748/wjg.v27.i3.281
Comparative study on artificial intelligence systems for detecting early esophageal squamous cell carcinoma between narrow-band and white-light imaging
Bing Li, Shi-Lun Cai, Wei-Min Tan, Ji-Chun Li, Ayimukedisi Yalikong, Xiao-Shuang Feng, Hon-Ho Yu, Pin-Xiang Lu, Zhen Feng, Li-Qing Yao, Ping-Hong Zhou, Bo Yan, Yun-Shi Zhong
Bing Li, Shi-Lun Cai, Ayimukedisi Yalikong, Li-Qing Yao, Ping-Hong Zhou, Yun-Shi Zhong, Department of Endoscopy Center, Zhongshan Hospital of Fudan University, Shanghai 200032, China
Wei-Min Tan, Ji-Chun Li, Bo Yan, School of Computer Science, Fudan University, Shanghai 200433, China
Xiao-Shuang Feng, Clinical Statistical Center, Shanghai Cancer Center of Fudan University, Shanghai 200032, China
Hon-Ho Yu, Department of Gastroenterology, Kiang Wu Hospital, Macau SAR 999078, China
Pin-Xiang Lu, Zhen Feng, Department of Endoscopy Center, Xuhui Hospital, Zhongshan Hospital of Fudan University, Shanghai 200031, China
Author contributions: Zhong YS and Yan B conceived the study design; Li B, Cai SL, Tan WM, Li JC, Yalikong A, Yu HH, Lu PX, and Feng Z acquired the data; Li B, Cai SL, Yalikong A, Tan WM, Feng XS, Yao LQ, Zhou PH, and Zhong YS analyzed and interpreted the data; Li B, Tan WM, and Cai SL drafted the manuscript; Zhong YS critically revised the manuscript for important intellectual content; Li B, Cai SL, Tan WM, Li JC, Yalikong A, Feng XS, Yu HH, Lu PX, Feng Z, Yao LQ, Zhou PH, Yan B, and Zhong YS approved the final version of the manuscript; Li B, Cai SL and Tan WM contributed equally to this article.
Supported by National Key R&D Program of China, No. 2018YFC1315000, No. 2018YFC1315005, No. 2019YFC1315800, and No. 2019YFC1315802; National Natural Science Foundation of China, No. 81861168036 and No. 81702305; Science and Technology Commission Foundation of Shanghai Municipality, No. 19411951600, and No. 19411951601; Macao SAR Science and Technology Development Foundation, No. 0023/2018/AFJ; and Dawn Program of Shanghai Education Commission, No. 18SG08.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of Zhongshan Hospital, Fudan University.
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to undergo treatment by written consent.
Conflict-of-interest statement: The authors declare that they have no competing interests.
Data sharing statement: The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.
STROBE statement: The authors have read the STROBE Statement checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Yun-Shi Zhong, MD, PhD, Professor, Department of Endoscopy Center, Zhongshan Hospital of Fudan University, No. 180 Fenglin Road, Shanghai 200032, China. zhongyunshi@yahoo.com
Received: October 9, 2020
Peer-review started: October 9, 2020
First decision: November 23, 2020
Revised: December 5, 2020
Accepted: December 22, 2020
Article in press: December 22, 2020
Published online: January 21, 2021
Processing time: 97 Days and 2.1 Hours
ARTICLE HIGHLIGHTS
Research background

Non-magnifying endoscopy with narrow-band imaging (NM-NBI) has been frequently used in routine screening of esophagus squamous cell carcinoma (ESCC). The performance of NBI for screening of early ESCC is, however, significantly affected by operator experience. Artificial intelligence may be a unique approach to compensate for the lack of operator experience.

Research motivation

In our previous research, we reported a novel system of computer-aided detection (CAD) to localize and identify early ESCC under conventional endoscopic white-light imaging (WLI) with sensitivity of above 97%. The construction of another CAD system for application in NM-NBI was the next step in the continuation of our research.

Research objectives

To construct a CAD system for application in NM-NBI to identify early ESCC and compare it with our previously reported CAD system with endoscopic WLI.

Research methods

We collected a total of 2167 abnormal NM-NBI images of early ESCC and 2568 normal images from three institutions (Zhongshan Hospital of Fudan University, Xuhui Hospital, and Kiang Wu Hospital) as the training dataset, and 316 pairs of images, each pair including images obtained with WLI and NBI (same part), were collected for validation. Twenty endoscopists participated in this study to review the validation images with or without the assistance of the CAD systems. The diagnostic results of the two CAD systems and the improvement in the diagnostic efficacy of endoscopists were compared in terms of sensitivity, specificity, accuracy, positive predictive value, and negative predictive value.

Research results

The area under receiver operating characteristic curve for CAD-NBI was 0.9761. For the validation dataset, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of CAD-NBI were 91.0%, 96.7%, 94.3%, 95.3%, and 93.6%, respectively, while those of CAD-WLI were 98.5%, 83.1%, 89.5%, 80.8%, and 98.7%, respectively. CAD-NBI showed superior accuracy and specificity than CAD-WLI (P = 0.028 and P ≤ 0.001, respectively), while CAD-WLI had higher sensitivity than CAD-NBI (P = 0.006). By using both CAD-WLI and CAD-NBI, the endoscopists could improve their diagnostic efficacy to the highest level, with accuracy, sensitivity, and specificity of 94.9%, 92.4%, and 96.7%, respectively.

Research conclusions

The CAD-NBI system for screening early ESCC has higher accuracy and specificity than CAD-WLI. Endoscopists can achieve the best diagnostic efficacy by using both CAD-WLI and CAD-NBI.

Research perspectives

According to the results, the two CAD systems had different advantages in avoiding missed diagnosis and excessive biopsy, which could help endoscopists, especially those with less experience, in screening of early ESCC more efficiently. As the two CAD systems have unique characteristics, we plan to develop a multichannel deep neural network to extract and combine the features of NBI and WLI simultaneously in our future work.