MINI REVIEW article
Front. Pharmacol.
Sec. Experimental Pharmacology and Drug Discovery
Volume 16 - 2025 | doi: 10.3389/fphar.2025.1697608
This article is part of the Research TopicAdvances in Biomarkers and Drug Targets: Harnessing Traditional and AI Approaches for Novel Therapeutic MechanismsView all 9 articles
Leveraging Artificial Intelligence to Validate Traditional Biomarkers and Drug Targets in Liver Cancer Recovery: A Mini Review
Provisionally accepted
- 1Southwest Medical University, Luzhou, China
- 2Department of Rehabilitation Medicine,The Affiliated Hospital of Southwest Medical University, Luzhou,sichuan, China
- 3Rehabilitation Medicine and Engineering Key Laboratory of Luzhou, Luzhou, China
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Hepatocellular carcinoma (HCC) remains a leading cause of cancer death, and recovery after therapy is shaped by heterogeneous etiologies, genomes and microenvironments. Targeted and immunotherapy combinations have broadened first-line options; yet durable benefit is uneven, and serum/imaging anchors (AFP, AFP-L3%, PIVKA-II, LI-RADS/mRECIST) incompletely resolve residual disease or functional restoration. In this review we summarise AI-enabled radiology, digital pathology and multi-omic/liquid-biopsy analytics that test and refine traditional biomarkers and drug-target readouts, and appraise translational opportunities in composite surveillance and recovery forecasting. We also discuss enduring challenges—including assay standardisation, spectrum bias, data leakage, domain shift and limited prospective external validation—that temper implementation. By integrating established anchors (AFP/AFP-L3%, PIVKA-II, ALBI, contrast-enhanced hallmarks) with AI-derived signals (radiomics/pathomics, cfDNA methylation) and pathway contexts (VEGF–VEGFR, WNT/β-catenin), emerging strategies align predictions with clinical endpoints, individualise therapy and chart hepatic function. Our synthesis provides an appraisal of AI–traditional integration in liver cancer recovery and outlines pragmatic standards—analytical robustness, transparent reporting and prospective, guideline-conformant evaluation—required for clinical adoption. We hope these insights will aid researchers and clinicians as they implement more effective, individualised monitoring and treatment pathways.
Keywords: Hepatocellular Carcinoma, artificial intelligence, Recovery, AFP, PIVKA-II, Radiomics
Received: 02 Sep 2025; Accepted: 06 Oct 2025.
Copyright: © 2025 Wu, Chen, Ji, Zhang, Xie and Liang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Bin Liang, binliang1027@126.com
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