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Predicting early recurrence of hepatocellular carcinoma after thermal ablation based on longitudinal MRI with a deep

Qingyang Kong1, Kai Li1

  • 1Department of Ultrasound, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.

The Oncologist
|March 20, 2025
PubMed
Summary

A deep learning model using longitudinal MRI accurately predicts early recurrence in hepatocellular carcinoma (HCC) patients after thermal ablation. The DL_Clinical model effectively stratifies patients by risk, aiding treatment decisions.

Keywords:
deep learningearly recurrencehepatocellular carcinomalongitudinal MRIthermal ablation

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Area of Science:

  • Medical Imaging and Artificial Intelligence
  • Hepatocellular Carcinoma Research
  • Oncology

Background:

  • Early recurrence (ER) prediction is crucial for improving prognosis in hepatocellular carcinoma (HCC) patients undergoing thermal ablation (TA).
  • Current methods require enhancement for accurate ER prediction post-TA.

Purpose of the Study:

  • To develop and validate a deep learning (DL) model system using longitudinal magnetic resonance imaging (MRI) for predicting ER in HCC patients after TA.
  • To integrate DL model signatures with clinical variables for enhanced risk stratification.

Main Methods:

  • Retrospective enrollment of 289 HCC patients who underwent TA from 2014-2017.
  • Development of two DL models (Pre and PrePost) using pre-operative and longitudinal MRI data.
  • Creation of an integrated DL_Clinical model combining the PrePost model signature with clinical variables for risk stratification.

Main Results:

  • The DL_Clinical model demonstrated superior performance in the external testing cohort with an AUC of 0.740, outperforming Clinical (0.571), Pre (0.648), and PrePost (0.689) models.
  • Significant difference in recurrence-free survival (RFS) was observed between high- and low-risk groups stratified by the DL_Clinical model (P=.04).

Conclusions:

  • Longitudinal MRI-based deep learning models, particularly the PrePost model, show excellent performance in predicting post-ablation ER for HCC.
  • The DL_Clinical model effectively stratifies HCC patients into high- and low-risk groups, supporting clinical decision-making for treatment and surveillance strategies.