Zhang JQ, Mi JJ, Wang R. Application of convolutional neural network-based endoscopic imaging in esophageal cancer or high-grade dysplasia: A systematic review and meta-analysis. World J Gastrointest Oncol 2023; 15(11): 1998-2016 [PMID: 38077641 DOI: 10.4251/wjgo.v15.i11.1998]
Corresponding Author of This Article
Rong Wang, MM, Chief Physician, Professor, Department of Gastroenterology, The Fifth Hospital of Shanxi Medical University (Shanxi Provincial People’s Hospital), No. 29 Shuangta West Street, Taiyuan 030012, Shanxi Province, China. wangxiongzai@126.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Meta-Analysis
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
World J Gastrointest Oncol. Nov 15, 2023; 15(11): 1998-2016 Published online Nov 15, 2023. doi: 10.4251/wjgo.v15.i11.1998
Application of convolutional neural network-based endoscopic imaging in esophageal cancer or high-grade dysplasia: A systematic review and meta-analysis
Jun-Qi Zhang, Jun-Jie Mi, Rong Wang
Jun-Qi Zhang, The Fifth Clinical Medical College, Shanxi Medical University, Taiyuan 030001, Shanxi Province, China
Jun-Jie Mi, Department of Gastroenterology, Shanxi Provincial People’s Hospital, Taiyuan 030012, Shanxi Province, China
Rong Wang, Department of Gastroenterology, The Fifth Hospital of Shanxi Medical University (Shanxi Provincial People’s Hospital), Taiyuan 030012, Shanxi Province, China
Author contributions: Zhang JQ conceived, designed the experiments and wrote a draft manuscript; Wang R analyzed, interpreted the results of the experiments and revised the manuscript; Mi JJ collected the clinical data and performed the experiments; and all authors read and approved the final manuscript.
Supported bythe Special Program for Science and Technology Cooperation and Exchange of Shanxi, No. 202104041101034.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Rong Wang, MM, Chief Physician, Professor, Department of Gastroenterology, The Fifth Hospital of Shanxi Medical University (Shanxi Provincial People’s Hospital), No. 29 Shuangta West Street, Taiyuan 030012, Shanxi Province, China. wangxiongzai@126.com
Received: August 1, 2023 Peer-review started: August 1, 2023 First decision: August 22, 2023 Revised: September 5, 2023 Accepted: October 11, 2023 Article in press: October 11, 2023 Published online: November 15, 2023 Processing time: 105 Days and 21.8 Hours
ARTICLE HIGHLIGHTS
Research background
The development of convolutional neural network (CNN) model as a novel diagnostic technology has promoted the screening, early detection, and improved prognosis of esophageal cancer and high-grade dysplasia (HGD).
Research motivation
Explore the diagnostic value of CNN model for esophageal cancer and HGD, and provide basis for its clinical application.
Research objectives
Conduct a meta-analysis of the diagnostic accuracy of CNN models for the diagnosis of esophageal cancer and HGD.
Research methods
We searched for relevant studies in various search engines, evaluated the diagnostic accuracy of CNN models, and calculated the diagnostic test accuracy with a bivariate method and hierarchical summary receiver operating characteristic method. Meta-regression and subgroup analyses were used to identify sources of heterogeneity.
Research results
After processing 28 items of still image-based and video-based analysis in statistics, CNN models have been proven to have high accuracy and diagnostic efficiency in diagnosing esophageal cancer or HGD and predicting the invasion depth of esophageal cancer.
Research conclusions
CNN-based image analysis in diagnosing esophageal cancer and HGD is an excellent diagnostic method with high sensitivity and specificity.
Research perspectives
A thorough evaluation of the accuracy of diagnosis in esophageal cancer and HGD requires further investigation. Large-scale trials are needed to assess performance and predict clinical values.
