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  5. Biomedical And Clinical Sciences
  7. Oncology And Carcinogenesis
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  11. Mr Radiomics To Predict Microvascular Invasion Status And Biological Process In Combined Hepatocellular Carcinoma-cholangiocarcinoma.

MR radiomics to predict microvascular invasion status and biological process in combined hepatocellular carcinoma-cholangiocarcinoma.

Yuyao Xiao1, Fei Wu1, Kai Hou1

  • 1Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China.

Insights Into Imaging
|July 9, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

This study developed an MRI-based radiomics model to predict microvascular invasion (MVI) in combined hepatocellular-cholangiocarcinoma (cHCC-CCA). The model shows prognostic value and reveals immune response pathways, aiding risk stratification and immunotherapy guidance.

Area of Science:

  • Radiomics and Medical Imaging
  • Oncology and Hepatobiliary Malignancies
  • Genomics and Immunobiology

Background:

  • Microvascular invasion (MVI) is a critical prognostic indicator in combined hepatocellular-cholangiocarcinoma (cHCC-CCA).
  • Reliable preoperative assessment of MVI remains challenging, impacting treatment decisions and patient stratification.
  • Magnetic Resonance Imaging (MRI) offers potential for non-invasive tumor characterization.

Purpose of the Study:

  • To develop and validate an MRI-based radiomics model for predicting MVI status in cHCC-CCA.
  • To investigate the prognostic significance of the radiomics model.
  • To explore the underlying biological processes associated with the radiomics signature.

Main Methods:

  • Retrospective and prospective datasets from two hospitals were utilized (total 143 patients).
Keywords:
Diagnosis criteriaLiver neoplasmsMagnetic resonance imagingPrognosis

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  • An MRI-based radiomics model was constructed using logistic regression and validated across training, validation, and test sets.
  • Prognostic value was assessed using Kaplan-Meier analysis; biological insights were gained through differential gene expression and Gene Ontology (GO) analysis.

    • Main Results:

    • The radiomics model demonstrated robust performance in predicting MVI status, with AUCs of 0.935 (training), 0.873 (validation), and 0.779 (test set).
    • Patients predicted as MVI-positive had significantly shorter overall survival (18 months) compared to MVI-negative patients (25 months; p=0.008).
    • Radiogenomic analysis linked the radiomics model to biological processes involved in immune response regulation.

    Conclusions:

    • An effective MRI-based radiomics model for MVI prediction in cHCC-CCA was established.
    • The model exhibits prognostic value and provides insights into immune-related biological processes.
    • This radiomics approach can aid in risk stratification and guide immunotherapy strategies for cHCC-CCA patients.