Publication Name	Artificial Intelligence in Medical Imaging
Manuscript ID	68145
Country	China

Received	2021-05-13 06:56
Peer-Review Started	2021-05-13 06:58
To Make the First Decision
Return for Revision	2021-06-02 13:56
Revised	2021-06-19 15:01
Second Decision	2021-06-30 00:36
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2021-06-30 00:42
Articles in Press	2021-06-30 00:42
Publication Fee Transferred
Edit the Manuscript by Language Editor	2021-07-08 17:46
Typeset the Manuscript	2021-07-09 05:24
Publish the Manuscript Online	2021-07-09 08:50

ISSN	2644-3260 (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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Category	Oncology
Manuscript Type	Minireviews
Article Title	Application of radiomics in hepatocellular carcinoma: A review
Manuscript Source	Invited Manuscript
All Author List	Zhi-Cheng Jin and Bin-Yan Zhong
ORCID	Author(s) ORCID Number Zhi-Cheng Jin http://orcid.org/0000-0002-6114-154X Bin-Yan Zhong http://orcid.org/0000-0001-9716-1211
Funding Agency and Grant Number
Corresponding Author	Bin-Yan Zhong, MD, PhD, Doctor, Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, 188 Shizi St, Suzhou 215006, Jiangsu Province, China. byzhongir@sina.com
Key Words	Hepatocellular carcinoma; Radiomics; Machine learning; Deep learning; Radiogenomics
Core Tip	The high molecular heterogeneity in hepatocellular carcinoma (HCC) poses huge challenges for clinical practice or trial design and has become a major barrier to improving the management of HCC. Radiomics method could quantify tumoral phenotypes and heterogeneity, which may provide benefits in clinical decision-making at a lower cost. Here, we review the workflow and application of radiomics in HCC.
Publish Date	2021-07-09 08:50
Citation	Jin ZC, Zhong BY. Application of radiomics in hepatocellular carcinoma: A review. Artif Intell Med Imaging 2021; 2(3): 64-72
URL	https://www.wjgnet.com/2644-3260/full/v2/i3/64.htm
DOI	https://dx.doi.org/10.35711/aimi.v2.i3.64

Full Article (PDF)	AIMI-2-64.pdf
Full Article (Word)	AIMI-2-64.docx
Manuscript File	68145_Review-FilipodiaCL.docx
Answering Reviewers	68145-Answering reviewers.pdf
Audio Core Tip	68145-Audio core tip.m4a
Conflict-of-Interest Disclosure Form	68145-Conflict-of-interest statement.pdf
Copyright License Agreement	68145-Copyright license agreement.pdf
Peer-review Report	68145-Peer-review(s).pdf
Scientific Misconduct Check	68145-Bing-Liu M-2.png
Scientific Misconduct Check	68145-Scientific misconduct check.pdf
Scientific Editor Work List	68145-Scientific editor work list.pdf