Development and multi-institutional validation of an artificial intelligence-based diagnostic system for gastric biopsy.

Journal: Cancer science
Published Date: Aug 12, 2022

Abstract

To overcome the increasing burden on pathologists in diagnosing gastric biopsies, we developed an artificial intelligence-based system for the pathological diagnosis of gastric biopsies (AI-G), which is expected to work well in daily clinical practice in multiple institutes. The multistage semantic segmentation for pathology (MSP) method utilizes the distribution of feature values extracted from patches of whole-slide images (WSI) like pathologists' "low-power view" information of microscopy. The training dataset included WSIs of 4511 gastric biopsy tissues from 984 patients. In tissue-level validation, MSP AI-G showed better accuracy (91.0%) than that of conventional patch-based AI-G (PB AI-G) (89.8%). Importantly, MSP AI-G unanimously achieved higher accuracy rates (0.946 ± 0.023) than PB AI-G (0.861 ± 0.078) in tissue-level analysis, when applied to the cohorts of 10 different institutes (3450 samples of 1772 patients in all institutes, 198-555 samples of 143-206 patients in each institute). MSP AI-G had high diagnostic accuracy and robustness in multi-institutions. When pathologists selectively review specimens in which pathologist's diagnosis and AI prediction are discordant, the requirement of a secondary review process is significantly less compared with reviewing all specimens by another pathologist.

Authors

  • Hiroyuki Abe
    Department of Pathology, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan.
  • Yusuke Kurose
    Research Center for Advanced Science and Technology, the University of Tokyo, Tokyo, Japan.
  • Shusuke Takahama
    Graduate School of Information Science and Technology, the University of Tokyo, Tokyo, Japan.
  • Ayako Kume
    Department of Pathology, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan.
  • Shu Nishida
    Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 1130033, Japan; Department of Pathology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 1130033, Japan.
  • Miyako Fukasawa
    Department of Pathology, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan.
  • Yoichi Yasunaga
    Department of Pathology, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan.
  • Tetsuo Ushiku
    Department of Pathology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan. usikut-tky@umin.ac.jp.
  • Youichiro Ninomiya
    Research Center for Medical Bigdata, National Institute of Informatics, Tokyo, Japan.
  • Akihiko Yoshizawa
    Japanese Society of Pathology, Tokyo, Japan.
  • Kohei Murao
    Research Centre for Medical Bigdata, National Institute of Informatics, Chiyoda-ku, Tokyo, Japan.
  • Shin'ichi Sato
    Research Center for Medical Bigdata, National Institute of Informatics, Tokyo, Japan.
  • Masaru Kitsuregawa
    Research Center for Medical Bigdata, National Institute of Informatics, Tokyo, Japan.
  • Tatsuya Harada
    Department of Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan. harada@mi.t.u-tokyo.ac.jp.
  • Masanobu Kitagawa
    Japanese Society of Pathology, Tokyo, Japan.
  • Masashi Fukayama
    Department of Pathology, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan.

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