The Value of Machine Learning-based Radiomics Model Characterized by PET Imaging with Ga-FAPI in Assessing Microvascular Invasion of Hepatocellular Carcinoma.

Journal: Academic radiology
PMID: 39648099

Abstract

RATIONALE AND OBJECTIVES: This study aimed to develop a radiomics model characterized by Ga-fibroblast activation protein inhibitors (FAPI) positron emission tomography (PET) imaging to predict microvascular invasion (MVI) of hepatocellular carcinoma (HCC). This study also investigated the impact of varying thresholds for maximum standardized uptake value (SUV) in semi-automatic delineation methods on the predictions of the model.

Authors

  • Rongqin Fan
    Department of Nuclear Medicine, Chongqing University Cancer Hospital, Chongqing 400030, PR China (R.F., X.L., X.C., D.C., R.Z.).
  • Xueqin Long
    Department of Nuclear Medicine, Chongqing University Cancer Hospital, Chongqing 400030, PR China (R.F., X.L., X.C., D.C., R.Z.).
  • Xiaoliang Chen
    State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, No.28, Xianning West Road, Xi'an, Shaanxi 710049, P.R. China.
  • Yanmei Wang
    CAS Key Laboratory of Soft Matter Chemistry, Department of Polymer Science and Engineering, University of Science and Technology of China, Hefei 230026, PR China. Electronic address: wangyanm@ustc.edu.cn.
  • Demei Chen
    Department of Nuclear Medicine, Chongqing University Cancer Hospital, Chongqing 400030, PR China (R.F., X.L., X.C., D.C., R.Z.).
  • Rui Zhou
    College of New Energy and Environment, Jilin University, Changchun 130021, China.

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