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World J Gastrointest Oncol. May 15, 2025; 17(5): 104410
Published online May 15, 2025. doi: 10.4251/wjgo.v17.i5.104410
Research status and progress of deep learning in automatic esophageal cancer detection
Jing Chen, Xin Fan, Qiao-Liang Chen, Wei Ren, Qi Li, Dong Wang, Jian He
Jing Chen, Xin Fan, Qiao-Liang Chen, Jian He, Department of Nuclear Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, Jiangsu Province, China
Wei Ren, The Comprehensive Cancer Center of Drum Tower Hospital, Medical School of Nanjing University & Clinical Cancer Institute of Nanjing University, Nanjing 210008, Jiangsu Province, China
Qi Li, Department of Pathology, Nanjing Drum Tower Hospital, Nanjing 210008, Jiangsu Province, China
Dong Wang, Nanjing Center for Applied Mathematics, Nanjing 211135, Jiangsu Province, China
Co-corresponding authors: Dong Wang and Jian He.
Author contributions: He J and Chen J conceived the idea for the manuscript; Chen J, Fan X, and Chen QL reviewed the literature and drafted the manuscript; Ren W and Li Q provided comprehensive and clinically relevant perspectives; He J and Wang D revised and finalized the manuscript, and contributed equally as co-corresponding authors; and all authors have read and approved the final version of the manuscript.
Supported by Funding for Clinical Trials from the Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 2021-LCYJ-MS-11.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
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: Jian He, MD, PhD, Associate Professor, Department of Nuclear Medicine, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing 210008, Jiangsu Province, China. hjxueren@126.com
Received: December 28, 2024
Revised: February 28, 2025
Accepted: March 24, 2025
Published online: May 15, 2025
Processing time: 138 Days and 13.7 Hours
Core Tip

Core Tip: Esophageal cancer (EC), a common malignant tumor, requires early detection for prognosis improvement. Deep learning (DL), particularly convolutional neural networks, has revolutionized EC diagnosis by enabling automated analysis of multimodal medical imaging, including digital pathology, endoscopy, and computed tomography. This article underscores the potential of DL to enhance screening accuracy and efficiency while addressing critical challenges such as constructing high-quality datasets, promoting multimodal feature fusion, validating model interpretability, and establishing dynamic evaluation systems. This article aims to guide future research and facilitate the clinical implementation of DL technologies in EC management.