Radiomics-Based AI for the Diagnosis and Prognosis of Vessels Encapsulating Tumor Clusters in Hepatocellular Carcinoma: Systematic Review and Meta-Analysis - Takeaways - MDSpire

Radiomics-Based AI for the Diagnosis and Prognosis of Vessels Encapsulating Tumor Clusters in Hepatocellular Carcinoma: Systematic Review and Meta-Analysis

  • By

  • Xuefeng Hua

  • Rongdang Fu

  • Ziwei Yin

  • July 2, 2026

  • 0 min

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  • 1

    Hepatocellular carcinoma (HCC) is the most prevalent primary malignant liver tumor, accounting for about 90% of liver cancer cases.

  • 2

    Vessels encapsulating tumor clusters (VETCs) are identified as an independent poor prognostic factor in HCC, linked to higher recurrence rates.

  • 3

    Traditional VETC diagnosis relies on invasive immunohistochemical analysis, which cannot be performed preoperatively and may miss positive areas.

  • 4

    AI technologies, particularly deep learning and radiomics, show promise for noninvasive prediction of VETC status and prognosis in HCC.

  • 5

    This systematic review aims to evaluate the diagnostic accuracy of AI models for predicting VETC status across multiple imaging modalities.

Original Source(s)

Radiomics-Based AI for the Diagnosis and Prognosis of Vessels Encapsulating Tumor Clusters in Hepatocellular Carcinoma: Systematic Review and Meta-Analysis

  • by Xuefeng Hua, Rongdang Fu, Ziwei Yin

  • July 2, 2026

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