| ISSN |
1948-5204 (online) |
| Open Access |
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/ |
| Copyright |
© The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Computer Science, Artificial Intelligence |
| Manuscript Type |
Minireviews |
| Article Title |
Research status and progress of deep learning in automatic esophageal cancer detection
|
| Manuscript Source |
Invited Manuscript |
| All Author List |
Jing Chen, Xin Fan, Qiao-Liang Chen, Wei Ren, Qi Li, Dong Wang and Jian He |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| Funding for Clinical Trials from the Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University |
2021-LCYJ-MS-11 |
|
| Corresponding Author |
Jian He, Associate Professor, MD, PhD, 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 |
| Key Words |
Esophageal cancer; Artificial intelligence; Deep learning; Automatic detection; Medical imaging |
| 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. |
| Publish Date |
2025-05-15 10:28 |
| Citation |
<p>Chen J, Fan X, Chen QL, Ren W, Li Q, Wang D, He J. Research status and progress of deep learning in automatic esophageal cancer detection. <i>World J Gastrointest Oncol</i> 2025; 17(5): 104410</p> |
| URL |
https://www.wjgnet.com/1948-5204/full/v17/i5/104410.htm |
| DOI |
https://dx.doi.org/10.4251/wjgo.v17.i5.104410 |
