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Articles Published Processes
1/28/2026 9:23:38 AM | Browse: 1 | Download: 0
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Received |
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2025-08-29 02:07 |
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Peer-Review Started |
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2025-08-29 02:07 |
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First Decision by Editorial Office Director |
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2025-10-22 09:06 |
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Return for Revision |
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2025-10-22 09:06 |
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Revised |
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2025-11-04 14:11 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2025-12-22 02:39 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2025-12-22 07:26 |
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Articles in Press |
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2025-12-22 07:26 |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2026-01-19 16:35 |
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Publish the Manuscript Online |
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2026-01-28 09:23 |
| ISSN |
1007-9327 (print) and 2219-2840 (online) |
| 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/ |
| Copyright |
© The Author(s) 2024. 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 |
Review |
| Article Title |
Deep learning techniques for using computed tomography imaging for hepatocellular carcinoma diagnosis, treatment and prognosis
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Yao Chen, Qiang Zhang and Ming-Yang Zhang |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Ming-Yang Zhang, School of Basic Medical Sciences, Nanchang University, No. 461 Bayi Avenue, Nanchang 330006, Jiangxi Province, China. zmmyipuyuan@163.com |
| Key Words |
Hepatocellular Carcinoma; Computed tomography; Deep learning; Diagnosis; Treatment; Prognosis |
| Core Tip |
This review systematically integrates deep learning technologies for hepatocellular carcinoma (HCC) based on computed tomography (CT) imaging, with a primary focus on tumor diagnosis, segmentation, predicting treatment response, and forecasting patient prognosis. Moreover, we reviewed popular deep learning networks in various fields and described the advantages of these prevalent deep learning models for different applications. Furthermore, we discussed the outstanding challenges in applying deep learning to extract information from CT images for the diagnosis and treatment of HCC patients. These insights could provide guidance for subsequent studies. |
| Publish Date |
2026-01-28 09:23 |
| Citation |
Chen Y, Zhang Q, Zhang MY. Deep learning techniques for using computed tomography imaging for hepatocellular carcinoma diagnosis, treatment and prognosis. World J Gastroenterol 2026; 32(5): 113592 |
| URL |
https://www.wjgnet.com/1007-9327/full/v32/i5/113592.htm |
| DOI |
https://dx.doi.org/10.3748/wjg.v32.i5.113592 |
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