Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Sep 21, 2020; 26(35): 5256-5271
Published online Sep 21, 2020. doi: 10.3748/wjg.v26.i35.5256
Artificial intelligence-assisted esophageal cancer management: Now and future
Yu-Hang Zhang, Lin-Jie Guo, Xiang-Lei Yuan, Bing Hu
Yu-Hang Zhang, Lin-Jie Guo, Xiang-Lei Yuan, Bing Hu, Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
Author contributions: Zhang YH reviewed literatures and drafted the manuscript; Guo LJ provided critical comments and revision; Yuan XL did part of the literature review; Hu B provided critical comments regarding artificial intelligence and revision.
Supported by Sichuan Science and Technology Department Key R and D Projects, No. 2019YFS0257; and Chengdu Technological Innovation R and D Projects, No. 2018-YFYF-00033-GX.
Conflict-of-interest statement: All authors declare no conflict of interests.
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:
Corresponding author: Bing Hu, MD, Chief Doctor, Professor, Department of Gastroenterology and Hepatology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Wuhou District, Chengdu 610041, Sichuan Province, China.
Received: May 25, 2020
Peer-review started: May 25, 2020
First decision: July 29, 2020
Revised: July 29, 2020
Accepted: August 12, 2020
Article in press: August 12, 2020
Published online: September 21, 2020

Esophageal cancer poses diagnostic, therapeutic and economic burdens in high-risk regions. Artificial intelligence (AI) has been developed for diagnosis and outcome prediction using various features, including clinicopathologic, radiologic, and genetic variables, which can achieve inspiring results. One of the most recent tasks of AI is to use state-of-the-art deep learning technique to detect both early esophageal squamous cell carcinoma and esophageal adenocarcinoma in Barrett’s esophagus. In this review, we aim to provide a comprehensive overview of the ways in which AI may help physicians diagnose advanced cancer and make clinical decisions based on predicted outcomes, and combine the endoscopic images to detect precancerous lesions or early cancer. Pertinent studies conducted in recent two years have surged in numbers, with large datasets and external validation from multi-centers, and have partly achieved intriguing results of expert’s performance of AI in real time. Improved pre-trained computer-aided diagnosis algorithms in the future studies with larger training and external validation datasets, aiming at real-time video processing, are imperative to produce a diagnostic efficacy similar to or even superior to experienced endoscopists. Meanwhile, supervised randomized controlled trials in real clinical practice are highly essential for a solid conclusion, which meets patient-centered satisfaction. Notably, ethical and legal issues regarding the black-box nature of computer algorithms should be addressed, for both clinicians and regulators.

Keywords: Artificial intelligence, Computer-aided diagnosis, Deep learning, Esophageal squamous cell cancer, Barrett’s esophagus, Endoscopy

Core Tip: Deep-learning-based artificial intelligence (AI) is a breakthrough technology that has been widely explored in diagnosis, treatment and prediction of esophageal cancer. Recent studies have dealt with limitations of previous researches, including small sample size, selection bias, lack of external validation and algorithm efficiency. Favorable outcomes that are comparable to experienced endoscopists have been achieved with satisfactory robustness, indicating a real-time potential. Future randomized controlled trials are needed to further address these issues concerning AI to provide an ultimate patient-centered satisfaction, in an interpretable, ethical and legal manner.