Convolutional neural network for classifying primary liver cancer based on triple-phase CT and tumor marker information: a pilot study.

Journal: Japanese journal of radiology
Published Date: Mar 10, 2021

Abstract

PURPOSE: To develop convolutional neural network (CNN) models for differentiating intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma (HCC) and predicting histopathological grade of HCC.

Authors

  • Hirotsugu Nakai
    Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate, School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan. Electronic address: nakai.hirotsugu.33x@kyoto-u.jp.
  • Koji Fujimoto
    Human Brain Research Center, Kyoto University Graduate School of Medicine, Kyoto, Japan.
  • Rikiya Yamashita
    Artera, Inc., Los Altos, CA.
  • Toshiyuki Sato
    Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan.
  • Yuko Someya
    Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan.
  • Kojiro Taura
    Division of Hepato-Biliary-Pancreatic Surgery and Transplantation, Department of Surgery, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan.
  • Hiroyoshi Isoda
    Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan; Preemptive Medicine and Lifestyle-Related Disease Research Center, Kyoto University Hospital, 54 Kawahara-cho, Shogoin, Sakyoku, Kyoto, 606-8507, Japan.
  • Yuji Nakamoto
    Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University.

Keywords

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