Ahn JC, Qureshi TA, Singal AG, Li D, Yang JD. Deep learning in hepatocellular carcinoma: Current status and future perspectives. World J Hepatol 2021; 13(12): 2039-2051 [PMID: 35070007 DOI: 10.4254/wjh.v13.i12.2039]
Corresponding Author of This Article
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
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
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/
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
Abstract
Hepatocellular carcinoma (HCC) is among the leading causes of cancer incidence and death. Despite decades of research and development of new treatment options, the overall outcomes of patients with HCC continue to remain poor. There are areas of unmet need in risk prediction, early diagnosis, accurate prognostication, and individualized treatments for patients with HCC. Recent years have seen an explosive growth in the application of artificial intelligence (AI) technology in medical research, with the field of HCC being no exception. Among the various AI-based machine learning algorithms, deep learning algorithms are considered state-of-the-art techniques for handling and processing complex multimodal data ranging from routine clinical variables to high-resolution 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.
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.
