| ISSN |
1948-5204 (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. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Review |
| Article Title |
Radiomics in hepatocellular carcinoma: A state-of-the-art review
|
| Manuscript Source |
Invited Manuscript |
| All Author List |
Shan Yao, Zheng Ye, Yi Wei, Han-Yu Jiang and Bin Song |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| National Natural Science Foundation of China |
81771797 |
| National Natural Science Foundation of China |
81971571 |
| Science and Technology Support Program of Sichuan Province |
2021YFS0021 |
| Science and Technology Support Program of Sichuan Province |
2021YFS0144 |
|
| Corresponding Author |
Bin Song, MD, PhD, Chief Doctor, Director, Director, Professor, Department of Radiology, West China Hospital, Sichuan University, No 37 Guoxue Alley, Chengdu 610041, Sichuan Province, China. cjr.songbin@vip.163.com |
| Key Words |
Hepatocellular carcinoma; Radiomics; Deep learning; Artificial intelligence; Medical imaging; Predictive modeling |
| Core Tip |
Medical imaging plays an indispensable role in hepatocellular carcinoma (HCC) clinical settings. Conventional imaging methods, however, provide limited and insufficient information. Recent studies have shown that radiomics and deep learning enable comprehensive insightful data mining that has achieved favorable performance in the detection and classification, diagnosis and differentiation, staging and grading, aggressive behavior, treatment responses, prognosis, and survival rates of HCC. Nevertheless, the wide implementation of radiomics and deep learning in actual routine clinical practice requires sustainable validation and optimization. |
| Publish Date |
2021-11-12 11:22 |
| Citation |
Yao S, Ye Z, Wei Y, Jiang HY, Song B. Radiomics in hepatocellular carcinoma: A state-of-the-art review. World J Gastrointest Oncol 2021; 13(11): 1599-1615 |
| URL |
https://www.wjgnet.com/1948-5204/full/v13/i11/1599.htm |
| DOI |
https://dx.doi.org/10.4251/wjgo.v13.i11.1599 |
