Meta-Analysis
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
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 by the 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
Abstract
BACKGROUND

Esophageal cancer is the seventh-most common cancer type worldwide, accounting for 5% of death from malignancy. Development of novel diagnostic techniques has facilitated screening, early detection, and improved prognosis. Convolutional neural network (CNN)-based image analysis promises great potential for diagnosing and determining the prognosis of esophageal cancer, enabling even early detection of dysplasia.

AIM

To conduct a meta-analysis of the diagnostic accuracy of CNN models for the diagnosis of esophageal cancer and high-grade dysplasia (HGD).

METHODS

PubMed, EMBASE, Web of Science and Cochrane Library databases were searched for articles published up to November 30, 2022. We evaluated the diagnostic accuracy of using the CNN model with still image-based analysis and with video-based analysis for esophageal cancer or HGD, as well as for the invasion depth of esophageal cancer. The pooled sensitivity, pooled specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and area under the curve (AUC) were estimated, together with the 95% confidence intervals (CI). A bivariate method and hierarchical summary receiver operating characteristic method were used to calculate the diagnostic test accuracy of the CNN model. Meta-regression and subgroup analyses were used to identify sources of heterogeneity.

RESULTS

A total of 28 studies were included in this systematic review and meta-analysis. Using still image-based analysis for the diagnosis of esophageal cancer or HGD provided a pooled sensitivity of 0.95 (95%CI: 0.92-0.97), pooled specificity of 0.92 (0.89-0.94), PLR of 11.5 (8.3-16.0), NLR of 0.06 (0.04-0.09), DOR of 205 (115-365), and AUC of 0.98 (0.96-0.99). When video-based analysis was used, a pooled sensitivity of 0.85 (0.77-0.91), pooled specificity of 0.73 (0.59-0.83), PLR of 3.1 (1.9-5.0), NLR of 0.20 (0.12-0.34), DOR of 15 (6-38) and AUC of 0.87 (0.84-0.90) were found. Prediction of invasion depth resulted in a pooled sensitivity of 0.90 (0.87-0.92), pooled specificity of 0.83 (95%CI: 0.76-0.88), PLR of 7.8 (1.9-32.0), NLR of 0.10 (0.41-0.25), DOR of 118 (11-1305), and AUC of 0.95 (0.92-0.96).

CONCLUSION

CNN-based image analysis in diagnosing esophageal cancer and HGD is an excellent diagnostic method with high sensitivity and specificity that merits further investigation in large, multicenter clinical trials.

Keywords: Esophageal cancer; High-grade dysplasia; Convolutional neural network; Deep learning; Systematic review; Meta-analysis

Core Tip: This systematic review provides a meta-analysis of 28 studies evaluating the accuracy of convolutional neural network (CNN) models for diagnosing esophageal cancer and high-grade dysplasia, and for predicting the invasion depth of esophageal cancer. It also establishes a theoretical foundation for the clinical application of CNN models. Based on this meta-analysis, CNN-based image analysis may have great potential for diagnosing and estimating the prognosis of esophageal cancer, though further study is needed.