Publication Name	World Journal of Gastroenterology
Manuscript ID	113592
Country	China

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
Manuscript Source	Invited Manuscript
All Author List	Yao Chen, Qiang Zhang and Ming-Yang Zhang
Funding Agency and Grant Number
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.
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 2025; In press

Received	2025-08-29 02:07
Peer-Review Started	2025-08-29 02:07
First Decision by Editorial Office Director	2025-10-22 09:06
Return for Revision	2025-10-22 09:06
Revised	2025-11-04 14:11
Publication Fee Transferred
Second Decision by Editor	2025-12-22 02:39
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2025-12-22 07:26
Articles in Press	2025-12-22 07:26
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1007-9327 (print) and 2219-2840 (online)
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