Posa A, Lippi M, Barbieri P, Andreani EV, Iezzi R. Performance of artificial intelligence in predicting hepatocellular carcinoma recurrence after thermal ablation: A systematic review. World J Hepatol 2025; 17(12): 111425 [DOI: 10.4254/wjh.v17.i12.111425]
Corresponding Author of This Article
Alessandro Posa, MD, Department of Diagnostic Imaging and Radiation Oncology, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Largo Agostino Gemelli 8, Rome 00168, Lazio, Italy. alessandro.posa@policlinicogemelli.it
Research Domain of This Article
Radiology, Nuclear Medicine & Medical Imaging
Article-Type of This Article
Systematic Reviews
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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/
Dec 27, 2025 (publication date) through Dec 29, 2025
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Journal Information of This Article
Publication Name
World Journal of Hepatology
ISSN
1948-5182
Publisher of This Article
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Posa A, Lippi M, Barbieri P, Andreani EV, Iezzi R. Performance of artificial intelligence in predicting hepatocellular carcinoma recurrence after thermal ablation: A systematic review. World J Hepatol 2025; 17(12): 111425 [DOI: 10.4254/wjh.v17.i12.111425]
Alessandro Posa, Marcello Lippi, Pierluigi Barbieri, Edoardo Vincenzo Andreani, Roberto Iezzi, Department of Diagnostic Imaging and Radiation Oncology, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Rome 00168, Lazio, Italy
Author contributions: Posa A, Lippi M and Barbieri P designed the study, analyzed the data and prepared the original draft; Posa A, Lippi M, Barbieri P, Andreani EV and Iezzi R reviewed and edited the draft; all authors have read and approved the final manuscript.
Conflict-of-interest statement: The authors declare no conflicts of interest.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised according to the PRISMA 2009 Checklist.
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: Alessandro Posa, MD, Department of Diagnostic Imaging and Radiation Oncology, Fondazione Policlinico Universitario Agostino Gemelli, IRCCS, Largo Agostino Gemelli 8, Rome 00168, Lazio, Italy. alessandro.posa@policlinicogemelli.it
Received: June 30, 2025 Revised: August 20, 2025 Accepted: November 13, 2025 Published online: December 27, 2025 Processing time: 180 Days and 13.6 Hours
Abstract
BACKGROUND
Recurrence prediction of hepatocellular carcinoma (HCC) after thermal ablation represents a challenge that can impact patients' quality of life. Artificial intelligence (AI)-based radiomics models applied to various imaging modalities can improve recurrence prediction, therefore guiding therapeutic decisions.
AIM
To evaluate the effectiveness of AI-driven predictive models in predicting HCC recurrence.
METHODS
A systematic literature search in PubMed and Scopus was performed, and a total of ten studies were included in this systematic review. All studies included response prediction evaluation with AI models for patients who underwent thermal ablation for HCC. Deep learning and machine learning algorithms were utilized to evaluate the predictive performance and accuracy through metrics such as the area under the curve and concordance index.
RESULTS
The developed models demonstrated high accuracy in predicting local progression and recurrence, allowing a solid risk stratification. In particular, the integration of imaging data and clinical-laboratory variables optimized treatment selection, highlighting the superior ability of imaging models to predict therapeutic outcomes compared to clinical parameters alone. Furthermore, radiomic analysis of follow-up imaging enabled highly accurate detection of ablation site recurrence.
CONCLUSION
AI-driven predictive models based on multimodal radiomic analyses integrated with clinical data represent promising tools for predicting tumor recurrence after thermal ablation in HCC patients.
Core Tip: Artificial intelligence can aid the prediction of post-thermal ablation treatment recurrence of hepatocellular carcinoma, improving patient's quality of life, tailoring the follow-up and avoiding unnecessary treatments while providing an early recognition of tumor recurrence.
