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Related Experiment Video

Updated: Jun 6, 2025

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Multimodal multiphasic preoperative image-based deep-learning predicts HCC outcomes after curative surgery.

Rex Wan-Hin Hui1, Keith Wan-Hang Chiu2, I-Cheng Lee3,4

  • 1Department of Medicine, School of Clinical Medicine, The University of Hong Kong, Hong Kong.

Hepatology (Baltimore, Md.)
|December 3, 2024
PubMed
Summary

A new deep-learning model, Recurr-NET, accurately predicts hepatocellular carcinoma (HCC) recurrence after surgery. This advanced tool outperforms traditional methods like microvascular invasion (MVI) assessment, offering improved preoperative prognostication for HCC patients.

Keywords:
CTHCCartificial intelligencedeep learninghepatic surgery

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Area of Science:

  • Hepatocellular Carcinoma (HCC) Research
  • Artificial Intelligence in Oncology
  • Medical Imaging Analysis

Background:

  • Hepatocellular carcinoma (HCC) recurrence is common after curative surgery.
  • Histological microvascular invasion (MVI) predicts recurrence but lacks preoperative utility.
  • Existing clinical prediction scores for HCC recurrence show variable performance.

Purpose of the Study:

  • To develop and validate Recurr-NET, a deep-learning model for predicting HCC recurrence.
  • To compare the predictive accuracy of Recurr-NET against MVI and clinical risk scores.
  • To assess the potential of Recurr-NET for preoperative prognostication in HCC.

Main Methods:

  • Developed Recurr-NET, a multimodal random survival forest deep-learning model.
  • Incorporated preoperative CT scans and clinical parameters from HCC patients.
  • Validated the model using internal (Hong Kong) and external (Taiwan) cohorts.

Main Results:

  • Recurr-NET demonstrated excellent accuracy in predicting HCC recurrence from 1 to 5 years (internal AUROC 0.770-0.857, external AUROC 0.758-0.798).
  • Recurr-NET significantly outperformed MVI and clinical risk scores (p < 0.001).
  • The model showed superior risk stratification for recurrence and mortality compared to MVI.

Conclusions:

  • Recurr-NET provides accurate preoperative prediction of HCC recurrence.
  • The model surpasses MVI and clinical scores in predictive performance.
  • Recurr-NET holds significant potential for improving preoperative prognostication in HCC management.