Su LY, Xu M, Chen YL, Lin MX, Xie XY. Ultrasomics in liver cancer: Developing a radiomics model for differentiating intrahepatic cholangiocarcinoma from hepatocellular carcinoma using contrast-enhanced ultrasound. World J Radiol 2024; 16(7): 247-255 [PMID: 39086609 DOI: 10.4329/wjr.v16.i7.247]
Corresponding Author of This Article
Xiao-Yan Xie, MD, PhD, Director, Department of Medical Ultrasound, The First Affiliated Hospital, Institute of Diagnostic and Interventional Ultrasound, Sun Yat-Sen University, No. 58 Zhongshan Road 2, Guangzhou 510000, Guangdong Province, China. xxy1992@21cn.com
Research Domain of This Article
Radiology, Nuclear Medicine & Medical Imaging
Article-Type of This Article
Retrospective Study
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/
World J Radiol. Jul 28, 2024; 16(7): 247-255 Published online Jul 28, 2024. doi: 10.4329/wjr.v16.i7.247
Ultrasomics in liver cancer: Developing a radiomics model for differentiating intrahepatic cholangiocarcinoma from hepatocellular carcinoma using contrast-enhanced ultrasound
Li-Ya Su, Ming Xu, Yan-Lin Chen, Man-Xia Lin, Xiao-Yan Xie
Li-Ya Su, Ming Xu, Yan-Lin Chen, Man-Xia Lin, Xiao-Yan Xie, Department of Medical Ultrasound, The First Affiliated Hospital, Institute of Diagnostic and Interventional Ultrasound, Sun Yat-Sen University, Guangzhou 510000, Guangdong Province, China
Co-first authors: Li-Ya Su and Ming Xu
Co-corresponding authors: Man-Xia Lin and Xiao-Yan Xie
Author contributions: Su LY, Xie XY, and Lin MX designed the research study; Su LY, Lin MX, Xu M, and Chen YL performed the research; Su LY and Chen YL analyzed the data and wrote the manuscript; Chen YL contributed to the model construction. All authors have read and approved the final manuscript.
Supported byNational Natural Science Foundation of China, No. 92059201.
Institutional review board statement: This study was reviewed and approved by the Ethics Committee of the First Affiliated Hospital of Sun Yat-Sen University.
Informed consent statement: Informed consent was waived for this research because of the retrospective design of the study.
Conflict-of-interest statement: We have no financial relationships to disclose.
Data sharing statement: No additional data are available.
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: Xiao-Yan Xie, MD, PhD, Director, Department of Medical Ultrasound, The First Affiliated Hospital, Institute of Diagnostic and Interventional Ultrasound, Sun Yat-Sen University, No. 58 Zhongshan Road 2, Guangzhou 510000, Guangdong Province, China. xxy1992@21cn.com
Received: January 16, 2024 Revised: May 10, 2024 Accepted: May 29, 2024 Published online: July 28, 2024 Processing time: 189 Days and 17.4 Hours
Core Tip
Core Tip: In this study, we successfully established a novel radiomics model that leveraged contrast-enhanced ultrasound (US) for accurate discrimination between intrahepatic cholangiocarcinoma and hepatocellular carcinoma. The refined radiomics model incorporated 21 essential features, surpassing the diagnostic accuracy of seasoned radiologists. This model excelled in diagnostic performance and ease of use, requiring only three specific time-point images and by a transparent image-acquisition protocol. Its implementation enhanced diagnostic objectivity and diminished the operator-dependence inherent in US examinations. This ultrasomics-based model can provide additional diagnostic insights to radiologists of varying levels of experience, thereby elevating overall diagnostic accuracy.