Deep learning 3D super-resolution radiomics model based on Gd-enhanced MRI for improving preoperative prediction of HCC pathological grading.

Journal: Abdominal radiology (New York)
Published Date: Jul 8, 2025

Abstract

PURPOSE: The histological grade of hepatocellular carcinoma (HCC) is an important factor associated with early tumor recurrence and prognosis after surgery. Developing a valuable tool to assess this grade is essential for treatment. This study aimed to evaluate the feasibility and efficacy of a deep learning-based three-dimensional super-resolution (SR) magnetic resonance imaging radiomics model for predicting the pathological grade of HCC.

Authors

  • Fei Jia
    Department of Radiation Oncology, The First Affiliated Hospital of Zhengzhou University and Department of Radiation Oncology, Basic Medical College of Zhengzhou University, Zhengzhou, Henan, China.
  • Baolin Wu
    Department of Radiology and Huaxi MR Research Center (HMRRC), Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, China; Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China. Electronic address: WBLlin0129@163.com.
  • Zhuo Wang
    Sichuan Center for Disease Control and Prevention, Chengdu 610500, China.
  • Jingqi Jiang
    Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, China.
  • Jinrui Liu
    Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, China.
  • Yang Liu
    Department of Computer Science, Hong Kong Baptist University, Hong Kong, China.
  • Yanhu Zhou
    Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, China.
  • Xuelian Zhao
    Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, China.
  • Wenxia Yang
    Department of Nuclear Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou 730030, China; Gansu Province Clinical Research Center for Functional and Molecular Imaging, Lanzhou 730030, China.
  • Yuhui Xiong
    GE HealthCare MR Research, Beijing, China.
  • Yanli Jiang
    Department of Magnetic Resonance, The Second Hospital & Clinical Medical School, Lanzhou University, Lanzhou, China. 61961048@qq.com.
  • Jing Zhang
    MOEMIL Laboratory, School of Optoelectronic Information, University of Electronic Science and Technology of China, Chengdu, China.

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