Baddam S. Advancing predictive oncology: Integrating clinical and radiomic models to optimize transarterial chemoembolization outcomes in hepatocellular carcinoma. World J Clin Cases 2025; 13(28): 109397 [DOI: 10.12998/wjcc.v13.i28.109397]
Corresponding Author of This Article
Sujatha Baddam, MD, Consultant, Researcher, Department of Internal Medicine, Huntsville Hospital, 101 Sivley Road SW, Huntsville, AL 35801, United States. drsujathabaddam@gmail.com
Research Domain of This Article
Oncology
Article-Type of This Article
Letter to the Editor
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/
Advancing predictive oncology: Integrating clinical and radiomic models to optimize transarterial chemoembolization outcomes in hepatocellular carcinoma
Sujatha Baddam
Sujatha Baddam, Department of Internal Medicine, Huntsville Hospital, Huntsville, AL 35801, United States
Author contributions: The author conceptualized, researched, and wrote the article. The author reviewed and approved the final version of the manuscript.
Conflict-of-interest statement: The author reports no relevant conflicts of interest for this article.
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: Sujatha Baddam, MD, Consultant, Researcher, Department of Internal Medicine, Huntsville Hospital, 101 Sivley Road SW, Huntsville, AL 35801, United States. drsujathabaddam@gmail.com
Received: May 12, 2025 Revised: May 20, 2025 Accepted: July 4, 2025 Published online: October 6, 2025 Processing time: 90 Days and 7.3 Hours
Abstract
This article discusses the innovative use of computed tomography radiomics combined with clinical factors to predict treatment response to first-line transarterial chemoembolization in hepatocellular carcinoma. Zhao et al developed a robust predictive model demonstrating high accuracy (area under the curve 0.92 in the training cohort) by integrating venous phase radiomic features with alpha-fetoprotein levels. This noninvasive approach enables early identification of patients unlikely to benefit from transarterial chemoembolization, allowing a timely transition to alternative therapies such as targeted agents or immunotherapy. Such precision strategies may improve clinical outcomes, optimize resource utilization, and increase survival in advanced hepatocellular carcinoma management. Future studies should emphasize external validation and broader clinical adoption.
Core Tip: Radiomic analysis of computed tomography images-particularly texture and shape features-combined with clinical biomarkers such as alpha-fetoprotein, enables accurate prediction of response to transarterial chemoembolization in hepatocellular carcinoma, with area under the curve values exceeding 0.90. These noninvasive models allow early identification of non-responders, support personalized treatment selection, and may improve outcomes through timely initiation of alternative therapies in liver cancer management.
