A Prospective Validation and Observer Performance Study of a Deep Learning Algorithm for Pathologic Diagnosis of Gastric Tumors in Endoscopic Biopsies.

Journal: Clinical cancer research : an official journal of the American Association for Cancer Research
Published Date: Nov 10, 2020

Abstract

PURPOSE: Gastric cancer remains the leading cause of cancer-related deaths in Northeast Asia. Population-based endoscopic screenings in the region have yielded successful results in early detection of gastric tumors. Endoscopic screening rates are continuously increasing, and there is a need for an automatic computerized diagnostic system to reduce the diagnostic burden. In this study, we developed an algorithm to classify gastric epithelial tumors automatically and assessed its performance in a large series of gastric biopsies and its benefits as an assistance tool.

Authors

  • Jeonghyuk Park
    VUNO Inc., Seocho-gu, Seoul, South Korea.
  • Bo Gun Jang
    Department of Pathology, Jeju National University School of Medicine and Jeju National University Hospital, Jeju, South Korea.
  • Yeong Won Kim
    VUNO Inc., Seocho-gu, Seoul, South Korea.
  • Hyunho Park
    VUNO, Seoul, Korea.
  • Baek-Hui Kim
    Department of Pathology, Korea University Guro Hospital, Guro-gu, Seoul, South Korea.
  • Myeung Ju Kim
    Department of Anatomy, Dankook University College of Medicine, Chonan, Chungnam, South Korea.
  • Hyungsuk Ko
    Department of Pathology, Green Cross Laboratories, Yongin, Gyeonggi, South Korea.
  • Jae Moon Gwak
    Department of Pathology, Green Cross Laboratories, Yongin, Gyeonggi, South Korea.
  • Eun Ji Lee
    Department of Pathology, Green Cross Laboratories, Yongin, Gyeonggi, South Korea.
  • Yul Ri Chung
    Department of Pathology, Green Cross Laboratories, Yongin, Gyeonggi, South Korea.
  • Kyungdoc Kim
    VUNO Inc., Seoul 06536, Korea.
  • Jae Kyung Myung
    Department of Pathology, College of Medicine, Hanyang University, Seongdong-gu, Seoul, South Korea.
  • Jeong Hwan Park
    Department of Pathology, Seoul National University College of Medicine and SMG-SNU Boramae Medical Center, Seoul, Korea.
  • Dong Youl Choi
    Department of Pathology, Green Cross Laboratories, Yongin, Gyeonggi, South Korea.
  • Chang Won Jung
    Department of Pathology, Green Cross Laboratories, Yongin, Gyeonggi, South Korea.
  • Bong-Hee Park
    Department of Pathology, Green Cross Laboratories, Yongin, Gyeonggi, South Korea.
  • Kyu-Hwan Jung
    VUNO Inc., Seoul, Korea.
  • Dong-Il Kim
    Department of Pathology, Green Cross Laboratories, Yongin, Gyeonggi, South Korea. khwan.jung@vuno.co ilkim@gclabs.co.kr.

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