Application of artificial intelligence in preoperative imaging of hepatocellular carcinoma: Current status and future perspectives

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, h...

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Published inWorld journal of gastroenterology : WJG Vol. 27; no. 32; pp. 5341 - 5350
Main Authors Feng, Bing, Ma, Xiao-Hong, Wang, Shuang, Cai, Wei, Liu, Xia-Bi, Zhao, Xin-Ming
Format Journal Article
LanguageEnglish
Published Baishideng Publishing Group Inc 28.08.2021
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Summary: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.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.
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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 by CAMS 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.
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
ISSN:1007-9327
2219-2840
2219-2840
DOI:10.3748/wjg.v27.i32.5341