Feng B, Ma XH, Wang S, Cai W, Liu XB, Zhao XM. Application of artificial intelligence in preoperative imaging of hepatocellular carcinoma: Current status and future perspectives. World J Gastroenterol 2021; 27(32): 5341-5350 [PMID: 34539136 DOI: 10.3748/wjg.v27.i32.5341]
Corresponding Author of This Article
Xiao-Hong Ma, MD, Associate Professor, Doctor, Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China. maxiaohong@cicams.ac.cn
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
Bing Feng, Xiao-Hong Ma, Shuang Wang, Wei Cai, Xin-Ming Zhao, Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China
Xia-Bi Liu, Beijing Laboratory of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China
Author contributions: Feng B performed literature review and drafted the manuscript; Cai W contributed to data collection of the study; Wang S, Liu XB, and Zhao XM reviewed the manuscript; Ma XH contributed to conception and design of the study, and critically revised this manuscript; all authors have read and approved the final manuscript.
Supported byCAMS Innovation Fund for Medical Sciences (CIFMS), No. 2016-I2M-1-001; PUMC Youth Fund, No. 2017320010; Chinese Academy of Medical Sciences (CAMS) Research Fund, No. ZZ2016B01; Beijing HopeRun Special Fund of Cancer Foundation of China, No. LC2016B15; and PUMC Postgraduate Education and Teaching Reform Fund, No. 10023201900303.
Conflict-of-interest statement: The authors declare no conflict of interests for this article.
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: Xiao-Hong Ma, MD, Associate Professor, Doctor, Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China. maxiaohong@cicams.ac.cn
Received: January 28, 2021 Peer-review started: January 28, 2021 First decision: March 29, 2021 Revised: April 15, 2021 Accepted: July 27, 2021 Article in press: July 27, 2021 Published online: August 28, 2021
Abstract
Hepatocellular carcinoma (HCC) is the most common primary malignant liver tumor in China. Preoperative diagnosis of HCC is challenging because of atypical imaging manifestations and the diversity of focal liver lesions. Artificial intelligence (AI), such as machine learning (ML) and deep learning, has recently gained attention for its capability to reveal quantitative information on images. Currently, AI is used throughout the entire radiomics process and plays a critical role in multiple fields of medicine. This review summarizes the applications of AI in various aspects of preoperative imaging of HCC, including segmentation, differential diagnosis, prediction of histopathology, early detection of recurrence after curative treatment, and evaluation of treatment response. We also review the limitations of previous studies and discuss future directions for diagnostic imaging of HCC.
Core Tip: Hepatocellular carcinoma (HCC) threatens human health because of its high morbidity and recurrence rates. Patients with HCC may benefit from early diagnosis, timely treatment, and appropriate follow-up strategies. In the era of big data, artificial intelligence (AI) provides critical information regarding the diagnosis, treatment, and prognosis of HCC. We herein discuss the role of AI in the following aspects of preoperative imaging: Segmentation, differential diagnosis, prediction of histopathology, early detection of recurrence after curative treatment, and evaluation of treatment response.
