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World J Hepatol. Dec 27, 2021; 13(12): 2039-2051
Published online Dec 27, 2021. doi: 10.4254/wjh.v13.i12.2039
Deep learning in hepatocellular carcinoma: Current status and future perspectives
Joseph C Ahn, Touseef Ahmad Qureshi, Amit G Singal, Debiao Li, Ju-Dong Yang
Joseph C Ahn, Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN 55904, United States
Touseef Ahmad Qureshi, Debiao Li, Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States
Amit G Singal, Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States
Ju-Dong Yang, Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States
Author contributions: Yang JD devised the project and the main conceptual ideas for the review; Ahn JC conducted the literature search and identified relevant studies to be included in the review; Qureshi TA and Li D provided the technical expertise on artificial intelligence and deep learning; Ahn JC drafted the manuscript; Yang JD and Singal AG revised the manuscript critically for important intellectual content; and all authors approved the final version to be published.
Conflict-of-interest statement: Dr. Yang provides a consulting service for Exact Sciences and Gilead; Dr. Singal has been on advisory boards and served as a consultant for Genentech, Bayer, Eisai, BMS, Exelixis, AstraZeneca, and TARGET RWE. No other potential conflicts of interest relevant to this article exist.
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/
Corresponding author: Ju-Dong Yang, MD, MS, Assistant Professor, Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, 8900 Beverly Blvd, Los Angeles, CA 90048, United States. judong.yang@cshs.org
Received: May 28, 2021
Peer-review started: May 28, 2021
First decision: July 6, 2021
Revised: July 19, 2021
Accepted: November 15, 2021
Article in press: November 15, 2021
Published online: December 27, 2021
Processing time: 212 Days and 8.3 Hours
Core Tip

Core Tip: There are emerging roles for deep learning technology in the field of hepatocellular carcinoma (HCC) including HCC risk prediction, as well as diagnosis, prognostication, and treatment planning leveraging readily available data from radiologic and histopathologic medical images. This article will provide a comprehensive review of the recently published studies that have applied deep learning for risk prediction, diagnosis, prognostication, and treatment planning for patients with HCC.