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CEUS-Based Microvascular Invasion Predictor in HCC: Improving Prognostic Stratification Following Thermal Ablation.

Keke Chen1, Shukang Zhang2, Tianjiao Huang3

  • 1Department of Ultrasound, Zhongshan Hospital, Fudan University, Shanghai, P. R. China.

Liver International : Official Journal of the International Association for the Study of the Liver
|April 28, 2026
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Summary

A deep learning model integrating contrast-enhanced ultrasound (CEUS) and clinical data accurately predicts microvascular invasion (MVI) risk in hepatocellular carcinoma (HCC) patients. This model also demonstrates prognostic value for recurrence-free survival (RFS) in thermal ablation (TA) cohorts.

Keywords:
contrast‐enhanced ultrasounddeep learninghepatocellular carcinomamicrovascular invasion; thermal ablation

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Area of Science:

  • Hepatocellular Carcinoma Research
  • Medical Imaging AI
  • Oncology Diagnostics

Background:

  • Microvascular invasion (MVI) is a critical factor influencing hepatocellular carcinoma (HCC) management and patient outcomes.
  • Accurate preoperative assessment of MVI risk is essential for optimal treatment planning in HCC.

Purpose of the Study:

  • To develop and validate a deep learning model for predicting MVI risk in HCC patients using contrast-enhanced ultrasound (CEUS) and clinical features.
  • To explore the prognostic significance of the developed MVI risk model in patients treated with thermal ablation (TA).

Main Methods:

  • A deep learning model was built using CEUS features (extracted via vision transformer) and clinical data from 688 HCC patients undergoing CEUS.
  • The model was validated internally and externally for MVI prediction in surgical resection (SR) candidates.
  • Recurrence-free survival (RFS) was analyzed in a TA cohort, comparing outcomes between model-stratified risk groups.

Main Results:

  • The MVI prediction model achieved high AUCs of 0.89 (internal) and 0.81 (external validation).
  • Key predictors included AFP-L3%, PIVKA-II, tumor size, and CEUS arterial-phase dynamics.
  • High MVI risk predicted by the model was independently associated with significantly lower RFS in TA patients (HR=2.90).

Conclusions:

  • The deep learning model effectively predicts MVI in HCC surgical candidates and shows prognostic value in TA patients.
  • The model's ability to stratify MVI risk offers potential for improved patient management and outcome prediction.
  • Prospective validation is recommended due to the retrospective, single-center nature of this study.