Zhao KF, Xie CB, Wu Y. Prediction of the efficacy of first transarterial chemoembolization for advanced hepatocellular carcinoma via a clinical-radiomics model. World J Clin Cases 2025; 13(23): 101742 [DOI: 10.12998/wjcc.v13.i23.101742]
Corresponding Author of This Article
Yang Wu, Doctor, Department of Intervention, The Second Affiliated Hospital of Zunyi Medical University, Intersection of Xinlong Avenue and Xinpu Avenue, Xinpu New District, Zunyi 563000, Guizhou Province, China. 1096945853@qq.com
Research Domain of This Article
Medicine, Research & Experimental
Article-Type of This Article
Clinical Trials 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 Clin Cases. Aug 16, 2025; 13(23): 101742 Published online Aug 16, 2025. doi: 10.12998/wjcc.v13.i23.101742
Prediction of the efficacy of first transarterial chemoembolization for advanced hepatocellular carcinoma via a clinical-radiomics model
Kai-Fei Zhao, Chao-Bang Xie, Yang Wu
Kai-Fei Zhao, Department of Radiology, The Affiliated Hospital of Zunyi Medical University, Zunyi 563000, Guizhou Province, China
Chao-Bang Xie, Department of Radiology, The Affiliated Hospital of Guizhou Medical University, Guiyang 563000, Guizhou Province, China
Yang Wu, Department of Intervention, The Second Affiliated Hospital of Zunyi Medical University, Zunyi 563000, Guizhou Province, China
Author contributions: Zhao KF and Xie CB contributed to conceptualization, methodology, data curation, writing, original draft; Wu Y contributed to visualization, validation, writing–review and editing. All authors approved the final manuscript and agreed to be accountable for all aspects of the work.
Institutional review board statement: This study was approved by the Ethics Committee of the Affiliated Hospital of Zunyi Medical University.
Informed consent statement: Since it was a single-center, retrospective, observational cohort study, informed consent was waived.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
CONSORT 2010 statement: The authors have read the CONSORT 2010 statement, and the manuscript was prepared and revised according to the CONSORT 2010 statement.
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: Yang Wu, Doctor, Department of Intervention, The Second Affiliated Hospital of Zunyi Medical University, Intersection of Xinlong Avenue and Xinpu Avenue, Xinpu New District, Zunyi 563000, Guizhou Province, China. 1096945853@qq.com
Received: September 26, 2024 Revised: March 9, 2025 Accepted: April 25, 2025 Published online: August 16, 2025 Processing time: 250 Days and 18.9 Hours
Core Tip
Core Tip: Hepatocellular carcinoma (HCC) is often diagnosed at advanced stages, where transarterial chemoembolization (TACE) serves as a key therapy. However, nearly 50% of patients show poor TACE response due to tumor heterogeneity. This study integrates preoperative computed tomography radiomics and clinical factors to build a predictive model for TACE efficacy. Radiomics noninvasively extracts quantitative imaging features reflecting tumor pathophysiology, enabling precise assessment of treatment response. The combined model stratifies patients by predicted risk, guiding timely transition to alternative therapies (e.g., targeted drugs or immunotherapy) for non-responders. This approach enhances personalized HCC management, optimizes resource allocation, and improves survival outcomes through data-driven clinical decision-making.
