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<p>Hepatocellular carcinoma (HCC) remains a major global health challenge characterized by profound biological heterogeneity inadequately captured by current staging systems. The Liver Imaging Reporting and Data System (LI-RADS), originally designed to standardize noninvasive diagnosis, has emerged as a clinically meaningful prognostic biomarker. Recent evidence from multicenter studies demonstrates that LI-RADS categories and imaging features reflect underlying tumor biology, correlating with microvascular invasion, molecular subclasses, and histologic subtypes. These features predict recurrence, overall survival, and therapeutic response across diverse treatment settings, including surgical resection, locoregional ablation, and systemic therapy. Notably, the 2024 LI-RADS Treatment Response Assessment (TRA) update specifically addresses the evaluation of radiation-based therapies, marking a critical evolution toward treatment-specific assessment. Integration with artificial intelligence and radiomics promises to further enhance prognostic accuracy and reproducibility. This paradigm shift positions LI-RADS at the forefront of precision oncology, offering a noninvasive means to assess tumor biology, guide personalized treatment decisions, and improve patient outcomes in the era of advanced HCC therapeutics.</p>

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Beyond diagnosis: evolving LI-RADS from a diagnostic tool to a prognostic biomarker in hepatocellular carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月12日

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  • Jeong Min Lee,
  • Xi Wei

摘要

Hepatocellular carcinoma (HCC) remains a major global health challenge characterized by profound biological heterogeneity inadequately captured by current staging systems. The Liver Imaging Reporting and Data System (LI-RADS), originally designed to standardize noninvasive diagnosis, has emerged as a clinically meaningful prognostic biomarker. Recent evidence from multicenter studies demonstrates that LI-RADS categories and imaging features reflect underlying tumor biology, correlating with microvascular invasion, molecular subclasses, and histologic subtypes. These features predict recurrence, overall survival, and therapeutic response across diverse treatment settings, including surgical resection, locoregional ablation, and systemic therapy. Notably, the 2024 LI-RADS Treatment Response Assessment (TRA) update specifically addresses the evaluation of radiation-based therapies, marking a critical evolution toward treatment-specific assessment. Integration with artificial intelligence and radiomics promises to further enhance prognostic accuracy and reproducibility. This paradigm shift positions LI-RADS at the forefront of precision oncology, offering a noninvasive means to assess tumor biology, guide personalized treatment decisions, and improve patient outcomes in the era of advanced HCC therapeutics.