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September 10, 2025Journal of Hepatocellular CarcinomaOpen Access

Triphasic CT Radiomics Model for Preoperative Prediction of Hepatocellular Carcinoma Pathological Grading

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Authors

HHHaibo HuangGuangzhou University of Chinese MedicineXPXianpan PanUnited Imaging Healthcare (China)YZYingdan ZhangGuangxi Medical University

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Overview

Retrospective analysis shows improved pathological grading prediction in hepatocellular carcinoma using a triphasic CT radiomics model.

Key Points

  • The triphasic CT radiomics model achieved AUCs of 0.890 for Edmondson-Steiner grading, enhancing prediction accuracy.
  • In the testing 2 dataset, the triphasic fusion model demonstrated an AUC of 0.871 for microvascular invasion grading, indicating strong performance.
  • Key features were extracted from 174 patients using algorithms like mRMR and LASSO, leading to robust model development.
  • This novel approach offers a noninvasive tool for preoperative prediction of pathological grading in hepatocellular carcinoma.

Cite This Study

Huang et al. (2025) studied this question.

synapsesocial.com/papers/68c1b60d54b1d3bfb60eb339https://doi.org/10.2147/jhc.s527056
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