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 [DOI: 10.3748/wjg.v32.i5.113592]
Corresponding Author of This Article
Ming-Yang Zhang, MD, Doctor, School of Basic Medical Sciences, Nanchang University, No. 461 Bayi Avenue, Nanchang 330006, Jiangxi Province, China. zmmyipuyuan@163.com
Research Domain of This Article
Computer Science, Artificial Intelligence
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Review
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This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
Feb 7, 2026 (publication date) through Jan 28, 2026
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Publication Name
World Journal of Gastroenterology
ISSN
1007-9327
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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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 [DOI: 10.3748/wjg.v32.i5.113592]
World J Gastroenterol. Feb 7, 2026; 32(5): 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, Qiang Zhang, Ming-Yang Zhang
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. zmmyipuyuan@163.com
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
Abstract
Hepatocellular carcinoma (HCC), the predominant form of primary liver cancer, significantly threatens to global health. Despite considerable advances in diagnostic and therapeutic approaches in recent years, the prognosis for patients with HCC remains unsatisfactory. The emergence of artificial intelligence (AI), particularly deep learning technologies, offers new hope for improving the diagnosis and treatment of HCC. Researchers have extensively explored ways to integrate deep learning models into the clinical management of HCC patients, which provides a valuable foundation for developing more personalized treatment strategies. Compared with other detection methods, computed tomography (CT) has attracted significant research interest because of its comprehensive advantages, including wide availability and high resolution, making it well suited for AI-powered analysis. This review systematically integrates deep learning technologies for HCC based on CT imaging, while focusing primarily on tumor diagnosis, segmentation, treatment response prediction, and patient prognosis prediction. Moreover, we review popular deep learning networks in various fields and describe the advantages of these prevalent deep learning models for different applications. Furthermore, we discuss 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.
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.
