Deep learning based on intratumoral heterogeneity predicts histopathologic grade of hepatocellular carcinoma.

Journal: BMC cancer
PMID: 40102774

Abstract

OBJECTIVES: The potential of medical imaging to non-invasively assess intratumoral heterogeneity (ITH) is increasingly being recognized. This study aimed to investigate the value of the ITH-based deep learning model for preoperative prediction of histopathologic grade in hepatocellular carcinoma (HCC).

Authors

  • Shaoming Song
    The First School of Clinical Medicine, Lanzhou University, Lanzhou, 730000, China.
  • Gong Zhang
    College of Communication Engineering, Jilin University, Changchun 130012, China.
  • Zhiyuan Yao
    Faculty of Hepatopancreatobiliary Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, China.
  • Ruiqiu Chen
    The First School of Clinical Medicine, Lanzhou University, Lanzhou, 730000, China.
  • Kai Liu
    College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China.
  • Tianchen Zhang
    Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Rd., Toronto, Ontario M5S 3G4, Canada.
  • Guineng Zeng
    Faculty of Hepatopancreatobiliary Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing, 100853, China.
  • Zizheng Wang
    Faculty of Hepatopancreatobiliary Surgery, First Medical Center of Chinese People's Liberation Army (PLA) General Hospital, 28 Fuxing Road, Beijing, 100853, China.
  • Rong Liu
    School of Biomedical Engineering, Dalian University of Technology, Dalian, China.

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