Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
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 by National 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
Abstract
BACKGROUND

Hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) represent the predominant histological types of primary liver cancer, comprising over 99% of cases. Given their differing biological behaviors, prognoses, and treatment strategies, accurately differentiating between HCC and ICC is crucial for effective clinical management. Radiomics, an emerging image processing technology, can automatically extract various quantitative image features that may elude the human eye. Reports on the application of ultrasound (US)-based radiomics methods in distinguishing HCC from ICC are limited.

AIM

To develop and validate an ultrasomics model to accurately differentiate between HCC and ICC.

METHODS

In our retrospective study, we included a total of 280 patients who were diagnosed with ICC (n = 140) and HCC (n = 140) between 1999 and 2019. These patients were divided into training (n = 224) and testing (n = 56) groups for analysis. US images and relevant clinical characteristics were collected. We utilized the XGBoost method to extract and select radiomics features and further employed a random forest algorithm to establish ultrasomics models. We compared the diagnostic performances of these ultrasomics models with that of radiologists.

RESULTS

Four distinct ultrasomics models were constructed, with the number of selected features varying between models: 13 features for the US model; 15 for the contrast-enhanced ultrasound (CEUS) model; 13 for the combined US + CEUS model; and 21 for the US + CEUS + clinical data model. The US + CEUS + clinical data model yielded the highest area under the receiver operating characteristic curve (AUC) among all models, achieving an AUC of 0.973 in the validation cohort and 0.971 in the test cohort. This performance exceeded even the most experienced radiologist (AUC = 0.964). The AUC for the US + CEUS model (training cohort AUC = 0.964, test cohort AUC = 0.955) was significantly higher than that of the US model alone (training cohort AUC = 0.822, test cohort AUC = 0.816). This finding underscored the significant benefit of incorporating CEUS information in accurately distinguishing ICC from HCC.

CONCLUSION

We developed a radiomics diagnostic model based on CEUS images capable of quickly distinguishing HCC from ICC, which outperformed experienced radiologists.

Keywords: Cholangiocarcinoma; Hepatocellular carcinoma; Contrast-enhanced ultrasound; Radiomics; Primary liver tumor

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.