Tu HB, Feng SY, Chen LH, Huang YJ, Zhang JZ, Peng SY, Lin DL, Ye XJ. Integrating ultrasound and serum indicators for evaluating outcomes of targeted immunotherapy in advanced liver cancer. World J Gastrointest Oncol 2025; 17(5): 105872 [DOI: 10.4251/wjgo.v17.i5.105872]
Corresponding Author of This Article
Xiao-Jian Ye, Associate Professor, Department of Ultrasound, The First Affiliated Hospital of Fujian Medical University, No. 20 Chazhong Road, Taijiang District, Fuzhou 350001, Fujian Province, China. xjian_y@163.com
Research Domain of This Article
Oncology
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/
Hai-Bin Tu, Si-Yi Feng, Li-Hong Chen, Yu-Jie Huang, Ju-Zhen Zhang, Su-Yu Peng, Department of Ultrasound, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, Fujian Province, China
Ding-Luan Lin, Department of Positron Emission Tomography, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, Fujian Province, China
Xiao-Jian Ye, Department of Ultrasound, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350001, Fujian Province, China
Xiao-Jian Ye, Department of Ultrasound, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350001, Fujian Province, China
Co-first authors: Hai-Bin Tu and Si-Yi Feng.
Author contributions: Tu HB contributed to conceptualization and wrote the paper; Feng SY performed the data statistical analysis; Tu HB and Feng SY contributed equally to this work as co-first authors; Chen LH, Huang YJ, Zhang JZ, Peng SY, and Lin DL followed the patients; Ye XJ revised the manuscript and gave guide for the study; and all authors read and approved the final manuscript.
Supported by Natural Science Foundation of Fujian Province, No. 2022J011285 and No. 2023J011480; Fuzhou Municipal Bureau of Science and Technology Program Fund, No. 2021-S-109; Provincial Subsidy Fund for Health and Wellness from Fujian Provincial Department of Finance, No. BPB-2022YXJ; and Fujian Provincial Health Technology Project, No. 2020GGB032.
Institutional review board statement: This study was reviewed and approved by Mengchao Hepatobiliary Hospital Ethics Committee (Approval No. 2021_084_01).
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The datasets generated and/or analyzed during the current study are not publicly available due to being generated based on information collected during clinical care but are available in de-identified form from the corresponding author(xjian_y@163.com) on reasonable request at the study’s close.
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-Jian Ye, Associate Professor, Department of Ultrasound, The First Affiliated Hospital of Fujian Medical University, No. 20 Chazhong Road, Taijiang District, Fuzhou 350001, Fujian Province, China. xjian_y@163.com
Received: February 10, 2025 Revised: March 27, 2025 Accepted: April 17, 2025 Published online: May 15, 2025 Processing time: 96 Days and 0.1 Hours
Core Tip
Core Tip: Predicting treatment response in patients with advanced hepatocellular carcinoma undergoing targeted immunotherapy remains a significant challenge. This study developed and validated the Target Immunotherapy Predictive Model, a novel, non-invasive tool that integrates pre-treatment ultrasound features (tumor diameter, changes in tumor stiffness) and accessible serum biomarkers (platelet count, white blood cell count) using machine learning. The Target Immunotherapy Predictive Model outperformed traditional staging systems in predicting treatment efficacy, providing a promising solution for personalized treatment selection in advanced hepatocellular carcinoma. This approach has the potential to minimize unnecessary treatments, optimize therapeutic strategies, and ultimately improve patient outcomes. A user-friendly web calculator further enhances its clinical applicability.
