Leveraging artificial intelligence to validate traditional biomarkers and drug targets in liver cancer recovery: a mini review

TL;DR

This paper reviews how artificial intelligence can improve traditional methods for monitoring liver cancer recovery and treatment by combining AI with existing biomarkers and new data sources.

Contribution

The paper introduces a framework for integrating AI with traditional biomarkers and multi-omic data to enhance liver cancer recovery monitoring and treatment personalization.

Findings

AI methods like radiomics and pathomics improve the accuracy of traditional biomarkers in liver cancer recovery.

Combining AI-derived signals with serum and imaging markers enhances individualized therapy and functional restoration predictions.

Abstract

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.

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