Advancing predictive oncology: Integrating clinical and radiomic models to optimize transarterial chemoembolization outcomes in hepatocellular carcinoma

doi:10.12998/wjcc.v13.i28.109397

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Oct 6, 2025 (publication date) through Aug 7, 2025

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World Journal of Clinical Cases

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2307-8960

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Letter to the Editor

World J Clin Cases. Oct 6, 2025; 13(28): 109397
Published online Oct 6, 2025. doi: 10.12998/wjcc.v13.i28.109397

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

Core Tip

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.