Review
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Nov 15, 2021; 13(11): 1599-1615
Published online Nov 15, 2021. doi: 10.4251/wjgo.v13.i11.1599
Radiomics in hepatocellular carcinoma: A state-of-the-art review
Shan Yao, Zheng Ye, Yi Wei, Han-Yu Jiang, Bin Song
Shan Yao, Zheng Ye, Yi Wei, Han-Yu Jiang, Bin Song, Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
Author contributions: Yao S and Song B designed the research; Yao S and Ye Z conducted literature search and analysis; Yao S wrote the paper; Yao S and Ye Z made critical revisions to the manuscript; Wei Y and Jiang HY provided material support; Song B provided funding for the article.
Supported by The Science and Technology Support Program of Sichuan Province, No. 2021YFS0021 and No. 2021YFS0144; and the National Natural Science Foundation of China, No. 81771797 and No. 81971571.
Conflict-of-interest statement: All authors declare no potential conflict of interests related to this publication.
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: Bin Song, MD, PhD, Chief Doctor, Director, Professor, Department of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu 610041, Sichuan Province, China. cjr.songbin@vip.163.com
Received: February 22, 2021
Peer-review started: February 22, 2021
First decision: April 19, 2021
Revised: April 22, 2021
Accepted: August 20, 2021
Article in press: August 20, 2021
Published online: November 15, 2021
Processing time: 262 Days and 19.5 Hours
Core Tip

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.