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©The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Feb 7, 2026; 32(5): 113592
Published online Feb 7, 2026. doi: 10.3748/wjg.v32.i5.113592
Published online Feb 7, 2026. doi: 10.3748/wjg.v32.i5.113592
Deep learning techniques for using computed tomography imaging for hepatocellular carcinoma diagnosis, treatment and prognosis
Yao Chen, Department of Pharmacy, Pengzhou Hospital of Traditional Chinese Medicine, Pengzhou 611930, Sichuan Province, China
Qiang Zhang, Department of Clinical Laboratory, Longgang District People’s Hospital of Shenzhen, Shenzhen 518172, Guangdong Province, China
Ming-Yang Zhang, School of Basic Medical Sciences, Nanchang University, Nanchang 330006, Jiangxi Province, China
Co-corresponding authors: Qiang Zhang and Ming-Yang Zhang.
Author contributions: Zhang MY designed the study; Chen Y and Zhang Q extracted data and wrote the original draft; Zhang MY and Zhang Q reviewed the manuscript; all authors have read and approved the final manuscript.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
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: Ming-Yang Zhang, MD, Doctor, School of Basic Medical Sciences, Nanchang University, No. 461 Bayi Avenue, Nanchang 330006, Jiangxi Province, China.
Received: August 29, 2025
Revised: November 4, 2025
Accepted: December 22, 2025
Published online: February 7, 2026
Processing time: 152 Days and 19.1 Hours
Revised: November 4, 2025
Accepted: December 22, 2025
Published online: February 7, 2026
Processing time: 152 Days and 19.1 Hours
Core Tip
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.