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Related Experiment Video

Updated: Jun 12, 2025

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Identification of macrotrabecular-massive hepatocellular carcinoma through multiphasic CT-based representation

Zhenyang Zhang1,2,3, Wanli Zhang1,2, Chutong He4

  • 1Department of Radiology, the Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China.

Medical Physics
|September 23, 2024
PubMed
Summary

Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) is aggressive. A novel multiphase contrast-enhanced CT (mpCECT) model accurately identifies MTM-HCC by fusing radiomics features, improving diagnostic potential.

Keywords:
computed tomographyfeature fusionmacrotrabecular‐massive hepatocellular carcinomaradiomics

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) is an aggressive subtype of hepatocellular carcinoma (HCC).
  • MTM-HCC is associated with poor patient survival.
  • Accurate identification of MTM-HCC is crucial for treatment planning.

Purpose of the Study:

  • To evaluate a novel representation learning-based feature fusion strategy for MTM-HCC identification.
  • To assess the performance of the multiphase contrast-enhanced CT (mpCECT)-based latent feature fusion (MCLFF) model.
  • To compare the MCLFF model against other fusion methods and conventional approaches.

Main Methods:

  • Retrospective analysis of 206 patients with surgically confirmed HCC.
  • Extraction of multiphasic radiomics features from mpCECT scans.
  • Development and validation of the MCLFF model using internal and external datasets.

Main Results:

  • The MCLFF model achieved a high AUC of 0.857 for MTM-HCC prediction by fusing arterial and portal venous phase features.
  • This performance surpassed all competing fusion models in internal validation.
  • Integrating additional radiological or clinical features further enhanced prediction accuracy.

Conclusions:

  • The MCLFF model demonstrates significant potential for accurate MTM-HCC prediction using fused multiphasic radiomics features.
  • The fusion of arterial and portal venous phase features is key to the model's efficacy.
  • Clinical factors and conventional radiological features offer incremental value to the MCLFF strategy.