Copyright
©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Dec 7, 2022; 28(45): 6363-6379
Published online Dec 7, 2022. doi: 10.3748/wjg.v28.i45.6363
Published online Dec 7, 2022. doi: 10.3748/wjg.v28.i45.6363
Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview
Pak Kin Wong, In Neng Chan, Liang Yao, Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
Hao-Ming Yan, School of Clinical Medicine, China Medical University, Shenyang 110013, Liaoning Province, China
Shan Gao, Department of Gastroenterology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, Hubei Province, China
Chi Hong Wong, Faculty of Medicine, Macau University of Science and Technology, Taipa 999078, Macau, China
Tao Yan, Zhong-Ren Wang, School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, Hubei Province, China
Liang Yao, Ying Hu, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
Hon Ho Yu, Department of Gastroenterology, Kiang Wu Hospital, Macau 999078, China
Author contributions: Wong PK, Chan IN, Yan HM, Gao S, Wong CH, and Yan T collected the literature and wrote the initial manuscript, conceptualized the table and figures, and contributed equally to this work; Yao L, Hu Y, Wang ZR, and Yu HH conceptualized the structure of the text and critically revised the manuscript for important intellectual content; all authors read and approved the final version of the manuscript.
Supported by the Guangdong Basic and Applied Basic Research Fund , Shenzhen Joint Fund (Guangdong-Shenzhen Joint Fund) Guangdong-Hong Kong-Macau Research Team Project, No. 2021B1515130003; Science and Technology Development Fund of Macau , No. 0026/2022/A ; and Project of Xiangyang Science and Technology on Medical and Health Field , No. 2022YL05A .
Conflict-of-interest statement: All authors declare no conflict of interest.
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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Tao Yan, PhD, Instructor, School of Mechanical Engineering, Hubei University of Arts and Science, No. 296 Longzhong Road, Xiangyang 441053, Hubei Province, China. yantao@hbuas.edu.cn
Received: September 20, 2022
Peer-review started: September 20, 2022
First decision: October 18, 2022
Revised: October 25, 2022
Accepted: November 16, 2022
Article in press: November 16, 2022
Published online: December 7, 2022
Peer-review started: September 20, 2022
First decision: October 18, 2022
Revised: October 25, 2022
Accepted: November 16, 2022
Article in press: November 16, 2022
Published online: December 7, 2022
Core Tip
Core Tip: Radiomics, especially deep-learning-based radiomics (DLR), has revolutionized the diagnosis, assessment and prognosis of gastrointestinal (GI) cancer. This review provides an analysis and status of DLR in GI cancer and identifies future challenges and recommendations.