Jin ZC, Zhong BY. Application of radiomics in hepatocellular carcinoma: A review. Artif Intell Med Imaging 2021; 2(3): 64-72 [DOI: 10.35711/aimi.v2.i3.64]
Corresponding Author of This Article
Bin-Yan Zhong, MD, PhD, Doctor, Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, No. 188 Shizi Street, Suzhou 215006, Jiangsu Province, China. byzhongir@sina.com
Research Domain of This Article
Oncology
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/
Artif Intell Med Imaging. Jun 28, 2021; 2(3): 64-72 Published online Jun 28, 2021. doi: 10.35711/aimi.v2.i3.64
Application of radiomics in hepatocellular carcinoma: A review
Zhi-Cheng Jin, Bin-Yan Zhong
Zhi-Cheng Jin, Center of Interventional Radiology and Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing 210009, Jiangsu Province, China
Bin-Yan Zhong, Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, Suzhou 215006, Jiangsu Province, China
Author contributions: Jin ZC and Zhong BY contributed to study design, review of literature, interpretation of data, and drafting and revision of the manuscript.
Conflict-of-interest statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential 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: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Bin-Yan Zhong, MD, PhD, Doctor, Department of Interventional Radiology, The First Affiliated Hospital of Soochow University, No. 188 Shizi Street, Suzhou 215006, Jiangsu Province, China. byzhongir@sina.com
Received: May 13, 2021 Peer-review started: May 13, 2021 First decision: June 2, 2021 Revised: June 19, 2021 Accepted: June 30, 2021 Article in press: June 30, 2021 Published online: June 28, 2021
Abstract
Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer with low 5-year survival rate. The high molecular heterogeneity in HCC poses huge challenges for clinical practice or trial design and has become a major barrier to improving the management of HCC. However, current clinical practice based on single bioptic or archived tumor tissue has been deficient in identifying useful biomarkers. The concept of radiomics was first proposed in 2012 and is different from the traditional imaging analysis based on the qualitative or semi-quantitative analysis by radiologists. Radiomics refers to high-throughput extraction of large amounts number of high-dimensional quantitative features from medical images through machine learning or deep learning algorithms. Using the radiomics method could quantify tumoral phenotypes and heterogeneity, which may provide benefits in clinical decision-making at a lower cost. Here, we review the workflow and application of radiomics in HCC.
Core Tip: The high molecular heterogeneity in hepatocellular carcinoma poses huge challenges for clinical practice or trial design and has become a major barrier to improving the management of hepatocellular carcinoma. Radiomics could quantify tumoral phenotypes and heterogeneity, which may provide benefits in clinical decision-making at a lower cost. Here, we review the workflow and application of radiomics in hepatocellular carcinoma.
