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Related Experiment Video

Updated: Aug 13, 2025

Non-Invasive PET/MR Imaging in an Orthotopic Mouse Model of Hepatocellular Carcinoma
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Radiomic Analysis Based on Magnetic Resonance Imaging for Predicting PD-L2 Expression in Hepatocellular Carcinoma.

Yun-Yun Tao1, Yue Shi1, Xue-Qin Gong1

  • 1Medical Imaging Key Laboratory of Sichuan Province, Interventional Medical Center, Department of Radiology, Medical Research Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China.

Cancers
|January 21, 2023
PubMed
Summary

Magnetic Resonance Imaging (MRI) radiomics can predict programmed death ligand-2 (PD-L2) expression in hepatocellular carcinoma (HCC) non-invasively. This approach aids in selecting patients for immunotherapy by identifying PD-L2 status before surgery.

Keywords:
MRIPD-L2hepatocellular carcinomaimmune checkpoint blockadeimmunotherapy targetradiomics

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Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging

Background:

  • Hepatocellular carcinoma (HCC) is a leading cause of cancer death globally.
  • Radiomics extracts quantitative imaging features for clinical decision-making.
  • Predicting programmed death ligand-2 (PD-L2) expression preoperatively is crucial for immunotherapy selection in HCC.

Purpose of the Study:

  • To evaluate the efficacy of MRI-based radiomic features for non-invasively predicting PD-L2 expression in HCC.
  • To establish a radiomic model for preoperative assessment of PD-L2 status.

Main Methods:

  • Retrospective analysis of 108 HCC patients.
  • Immunohistochemistry to determine PD-L2 expression levels.
  • Extraction of radiomic features from preoperative MRI (T2-weighted, arterial, portal venous phases) using 3D-Slicer.
  • Feature selection via LASSO and model construction using logistic regression with cross-validation.
  • Performance evaluation using Area Under the Receiver Characteristic Curve (AUC).

Main Results:

  • Fifty HCC cases showed high PD-L2 expression, and 58 showed low expression.
  • Radiomic features significantly correlated with PD-L2 expression.
  • The combined MRI radiomics model achieved the highest AUC of 0.871 (95% CI: 0.803-0.939), outperforming individual phase models.

Conclusions:

  • MRI radiomics can effectively and non-invasively predict PD-L2 expression in HCC prior to surgery.
  • This predictive capability offers valuable guidance for selecting patients eligible for immune checkpoint blockade therapy.