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The nVR model: prediction of LI-RADS treatment response nonviable recurrence of hepatocellular carcinoma after primary transarterial chemoembolization

  • 0Department of Radiology, Nurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Zhongda Hospital, Southeast University, Nanjing, China.
Abdominal Radiology (new York) +

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April 11, 2025

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April 11, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study developed a prediction model for early tumor recurrence after transarterial chemoembolization (TACE) in hepatocellular carcinoma (HCC) patients. The non-Viable Recurrence (nVR) model uses MRI features to identify high-risk lesions for improved patient management.

Area Of Science

  • Radiology
  • Oncology
  • Hepatocellular Carcinoma Research

Background

  • Transarterial chemoembolization (TACE) is a standard treatment for hepatocellular carcinoma (HCC).
  • Predicting early tumor recurrence after TACE is crucial for timely treatment adjustments.
  • Identifying nonviable lesions on initial follow-up imaging requires further risk stratification.

Purpose Of The Study

  • To develop and validate a predictive model for early tumor recurrence (within 12 months) in LR-TR nonviable lesions after TACE.
  • To integrate MRI features and clinical data for enhanced prediction accuracy.
  • To stratify patients into risk groups for personalized management.

Main Methods

  • Multicenter retrospective study including HCC patients who underwent TACE.
  • Evaluation of baseline and first follow-up MRI features for lesions classified as LR-TR nonviable.
  • Development of a logistic regression model (nVR model) incorporating key imaging predictors.
  • 5-fold cross-validation and Kaplan-Meier analysis for model validation and relapse-free survival assessment.

Main Results

  • Non-smooth lesion margins and peritumoral hyperintensity on T2/DWI were independent predictors of early recurrence.
  • The developed non-Viable Recurrence (nVR) model demonstrated good predictive performance (AUC 0.759 training, 0.765 validation).
  • The nVR model effectively stratified patients into high- and low-risk groups with significantly different relapse-free survival.

Conclusions

  • The nVR model, utilizing two radiologic features, reliably predicts early post-TACE recurrence in LR-TR nonviable HCC lesions.
  • This model aids in identifying patients at high risk for early recurrence, facilitating prompt clinical intervention.
  • The findings support the use of specific MRI features for improved prognostication after TACE.

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