Publication Name	World Journal of Hepatology
Manuscript ID	68616
Country	United States

Received	2021-05-28 21:03
Peer-Review Started	2021-05-28 21:06
To Make the First Decision
Return for Revision	2021-07-06 01:56
Revised	2021-07-19 21:14
Second Decision	2021-11-12 03:21
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2021-11-15 03:08
Articles in Press	2021-11-15 03:08
Publication Fee Transferred
Edit the Manuscript by Language Editor	2021-11-09 22:27
Typeset the Manuscript	2021-12-13 08:57
Publish the Manuscript Online	2021-12-27 05:24

ISSN	1948-5182 (online)
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/
Copyright	© The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
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Website	http://www.wjgnet.com

Category	Gastroenterology & Hepatology
Manuscript Type	Minireviews
Article Title	Deep learning in hepatocellular carcinoma: Current status and future perspectives
Manuscript Source	Invited Manuscript
All Author List	Joseph C Ahn, Touseef Ahmad Qureshi, Amit G Singal, Debiao Li and Ju-Dong Yang
Funding Agency and Grant Number
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
Key Words	Hepatocellular carcinoma; Artificial intelligence; Deep learning
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.
Publish Date	2021-12-27 05:24
Citation	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
URL	https://www.wjgnet.com/1948-5182/full/v13/i12/2039.htm
DOI	https://dx.doi.org/10.4254/wjh.v13.i12.2039

Full Article (PDF)	WJH-13-2039.pdf
Full Article (Word)	WJH-13-2039.docx
Manuscript File	68616_Auto_Edited-JJ Wang-Webster J-Clear.docx
Answering Reviewers	68616-Answering reviewers.pdf
Audio Core Tip	68616-Audio core tip.m4a
Conflict-of-Interest Disclosure Form	68616-Conflict-of-interest statement.pdf
Copyright License Agreement	68616-Copyright license agreement.pdf
Peer-review Report	68616-Peer-review(s).pdf
Scientific Misconduct Check	68616-Bing-Wang JJ-2.png
Scientific Editor Work List	68616-Scientific editor work list.pdf