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Preoperative Computed Tomography Radiomics-Based Models for Predicting Microvascular Invasion of Intrahepatic

Yong Zhu1, Jiao Chen1, Wenjing Cui1

  • 1Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu Province, China.

Journal of Computer Assisted Tomography
|January 6, 2025
PubMed
Summary

Preoperative CT radiomics effectively predicts microvascular invasion (MVI) in intrahepatic mass-forming cholangiocarcinoma (IMCC). A combined radiomics and radiologic model demonstrated optimal predictive performance for MVI.

Keywords:
AFP - alpha-fetoproteinCA 19-9 - carbohydrate antigen 19-9DP - delayed phaseHAP - hepatic arterial phaseICC - intrahepatic cholangiocarcinomaIMCC - intrahepatic mass-forming cholangiocarcinomaLASSO - least absolute shrinkage and selection operatorMVI - microvascular invasionPVP - portal venous phasecomputed tomographyintrahepatic cholangiocarcinomamicrovascular invasionnomogramradiomics

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Area of Science:

  • Radiology
  • Medical Imaging
  • Oncology

Background:

  • Intrahepatic mass-forming cholangiocarcinoma (IMCC) is a challenging liver cancer.
  • Microvascular invasion (MVI) is a critical prognostic factor in IMCC.
  • Accurate preoperative prediction of MVI is essential for treatment planning.

Purpose of the Study:

  • To evaluate the predictive capability of preoperative CT-based radiomics for MVI in IMCC.
  • To develop and validate radiomics-based models for MVI prediction.

Main Methods:

  • Retrospective review of 121 IMCC patients' preoperative CT data.
  • Extraction and selection of radiomics features using LASSO regression.
  • Development of radiomics, radiologic, and combined prediction models.

Main Results:

  • 16 stable radiomics features were selected from 3948 extracted features.
  • The combined model achieved an AUC of 0.958 (training) and 0.829 (test) for MVI prediction.
  • The combined model incorporated shape, intratumoral vessels, and CT ratio.

Conclusions:

  • CT radiomics signature is a powerful predictor of MVI in IMCC.
  • The developed preoperative combined model shows excellent performance in predicting MVI.