Deep learning and digital pathology powers prediction of HCC development in steatotic liver disease.

Journal: Hepatology (Baltimore, Md.)
PMID: 38768142

Abstract

BACKGROUND AND AIMS: Identifying patients with steatotic liver disease who are at a high risk of developing HCC remains challenging. We present a deep learning (DL) model to predict HCC development using hematoxylin and eosin-stained whole-slide images of biopsy-proven steatotic liver disease.

Authors

  • Takuma Nakatsuka
    Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Ryosuke Tateishi
    Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Masaya Sato
    Graduate School of Science and Technology, Meijo University.
  • Natsuka Hashizume
    RWD Analytics, Healthcare & Life Science, IBM Japan Ltd., Tokyo, Japan.
  • Ami Kamada
    RWD Analytics, Healthcare & Life Science, IBM Japan Ltd., Tokyo, Japan.
  • Hiroki Nakano
    Healthcare Analytics, Global Business Services, IBM Japan Ltd, Tokyo, Japan.
  • Yoshinori Kabeya
    Healthcare Analytics, Global Business Services, IBM Japan Ltd, Tokyo, Japan.
  • Sho Yonezawa
    Healthcare Analytics, Global Business Services, IBM Japan Ltd, Tokyo, Japan.
  • Rie Irie
    Department of Pathology, Keio University School of Medicine, Tokyo, Japan.
  • Hanako Tsujikawa
    Department of Pathology, Keio University School of Medicine, Tokyo, Japan.
  • Yoshio Sumida
    Division of Hepatology and PancreatologyDepartment of Internal MedicineAichi Medical UniversityAichiJapan.
  • Masashi Yoneda
    Division of Hepatology and PancreatologyDepartment of Internal MedicineAichi Medical UniversityAichiJapan.
  • Norio Akuta
    Department of Hepatology, Toranomon Hospital and Okinaka Memorial Institute for Medical Research, Tokyo, Japan.
  • Takumi Kawaguchi
    Division of GastroenterologyDepartment of MedicineKurume University School of MedicineKurumeJapan.
  • Hirokazu Takahashi
    Department of Internal MedicineSaga Medical SchoolSaga UniversitySagaJapan.
  • Yuichiro Eguchi
    Department of Internal MedicineSaga Medical SchoolSaga UniversitySagaJapan.
  • Yuya Seko
    Department of Gastroenterology and HepatologyKyoto Prefectural University of MedicineKyotoJapan.
  • Yoshito Itoh
    Department of Gastroenterology and HepatologyKyoto Prefectural University of MedicineKyotoJapan.
  • Eisuke Murakami
    Department of Gastroenterology and Metabolism, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan.
  • Kazuaki Chayama
    Department of Gastroenterology and MetabolismApplied Life SciencesInstitute of Biomedical & Health SciencesHiroshima UniversityHiroshimaJapan.
  • Makiko Taniai
    Department of Internal Medicine, Institute of Gastroenterology, Tokyo Women's Medical University, Tokyo, Japan.
  • Katsutoshi Tokushige
    Department of Internal Medicine, Institute of Gastroenterology, Tokyo Women's Medical University, Tokyo, Japan.
  • Takeshi Okanoue
    Hepatology CenterSaiseikai Suita HospitalSuitaJapan.
  • Michiie Sakamoto
    Department of Pathology, Keio University School of Medicine, Tokyo, Japan.
  • Mitsuhiro Fujishiro
    Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan; Department of Endoscopy and Endoscopic Surgery, The University of Tokyo, Tokyo, Japan.
  • Kazuhiko Koike
    Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

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