Meena BL, Behera B, Rudra OS, Sharma D. Artificial intelligence in prognostication and treatment response modeling in advanced hepatocellular carcinoma. Artif Intell Gastroenterol 2026; 7(2): 120311 [DOI: 10.35712/aig.120311]
Corresponding Author of This Article
Babu Lal Meena, DM, MD, Department of Hepatology, Postgraduate Institute of Medical Education and Research, Sector 12, Chandigarh 160012, India. drbabupgi@gmail.com
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
review-article
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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/
Artif Intell Gastroenterol. Aug 8, 2026; 7(2): 120311 Published online Aug 8, 2026. doi: 10.35712/aig.120311
Artificial intelligence in prognostication and treatment response modeling in advanced hepatocellular carcinoma
Babu Lal Meena, Bijaylaxmi Behera, Omkar S Rudra, Deepti Sharma
Babu Lal Meena, Department of Hepatology, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India
Bijaylaxmi Behera, Department of Pediatrics, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India
Omkar S Rudra, Department of Hepatology, Institute of Liver and Biliary Sciences, Vasant Kunj, New Delhi 110070, India
Deepti Sharma, Department of Radiation Oncology, Institute of Liver and Biliary Sciences, Vasant Kuunj, New Delhi 110070, India
Author contributions: Meena BL conceptualized the manuscript, designed the study framework, and prepared the initial draft; Behera B, Rudra OS, and Sharma D contributed to the literature review and critically revised the manuscript for important intellectual content. All authors approved the final version of the manuscript.
AI contribution statement: The authors were solely responsible for the conception, literature review, interpretation, and critical analysis of the manuscript. All scientific content and conclusions were developed and validated by the authors. AI tools (including ChatGPT and Grammarly) were used only for language refinement, structuring of text, and improvement of clarity. No part of the manuscript was generated independently by AI without author oversight and revision. AI tools were not involved in study design, data analysis, interpretation of results, or generation of scientific content. No images or figures were generated using AI; all figures were created by the authors using Microsoft PowerPoint and are provided in editable format.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Babu Lal Meena, DM, MD, Department of Hepatology, Postgraduate Institute of Medical Education and Research, Sector 12, Chandigarh 160012, India. drbabupgi@gmail.com
Received: February 24, 2026 Revised: April 19, 2026 Accepted: May 22, 2026 Published online: August 8, 2026 Processing time: 163 Days and 15.9 Hours
Abstract
Advanced hepatocellular carcinoma (HCC) presents a dual challenge of tumor progression and underlying hepatic dysfunction, resulting in substantial heterogeneity in survival and treatment response. Artificial intelligence (AI) has been increasingly applied to address limitations of conventional stage-based systems; however, its impact on clinical decision-making remains uncertain. We conducted a narrative critical review of primary validation studies and high-quality systematic reviews (2020-2026) evaluating AI-based survival prediction and treatment response modeling in advanced HCC. We focused on methodological rigor, external and temporal validation, calibration, causal inference, and translational readiness. Several retrospective studies report improved discrimination for radiomic, deep learning, and multimodal machine learning models compared with conventional frameworks such as Barcelona Clinic Liver Cancer staging and albumin-bilirubin grade. Absolute area under the receiver operating characteristic curve improvements of approximately 0.14-0.40 have been reported in selected cohorts. These models improve intra-stage risk stratification and have the potential to predict response to systemic and locoregional therapies. However, most studies remain retrospective and focus primarily on prognostic rather than predictive modeling. External validation, calibration assessment, and competing-risk adaptation are inconsistently reported, and causal approaches to estimate treatment-effect heterogeneity are rarely used. Evidence demonstrating improvement in therapeutic allocation or patient-centered outcomes remains limited. AI-based modeling in advanced HCC offers improved risk stratification, yet improved discrimination metrics alone do not establish clinical utility. Prospective validation, causal inference frameworks, standardized reporting, and demonstration of decision impact are essential prerequisites for clinical integration. Without such rigor, AI may remain technologically sophisticated yet clinically unproven.
Core Tip: Artificial intelligence models in advanced hepatocellular carcinoma frequently demonstrate improved discrimination compared with conventional staging systems; however, most remain retrospective, prognostic rather than predictive, and inconsistently validated across institutions and therapeutic eras. This review critically distinguishes statistical performance gains from clinically meaningful advancement, emphasizing the need for causal modeling, competing-risk adaptation, calibration assessment, and prospective decision-impact evaluation before routine integration into multidisciplinary hepatology practice.
