A collaborative workflow between pathologists and deep learning for the evaluation of tumour cellularity in lung adenocarcinoma.

Journal: Histopathology
Published Date: Sep 12, 2022

Abstract

AIMS: The reporting of tumour cellularity in cancer samples has become a mandatory task for pathologists. However, the estimation of tumour cellularity is often inaccurate. Therefore, we propose a collaborative workflow between pathologists and artificial intelligence (AI) models to evaluate tumour cellularity in lung cancer samples and propose a protocol to apply it to routine practice.

Authors

  • Taro Sakamoto
    Department of Pathology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.
  • Tomoi Furukawa
    Department of Pathology, Nagasaki University Graduate School of Biomedical Sciences, Sakamoto, Nagasaki, Japan.
  • Hoa H N Pham
    Department of Pathology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.
  • Kishio Kuroda
    Department of Pathology, Nagasaki University Graduate School of Biomedical Sciences, Sakamoto, Nagasaki, Japan.
  • Kazuhiro Tabata
    Department of Pathology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.
  • Yukio Kashima
    Department of Pathology, Awaji Medical Center, Sumoto, Japan.
  • Ethan N Okoshi
    Department of Pathology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.
  • Shimpei Morimoto
    Innovation Platform and Office for Precision Medicine (iPOP), Graduate School of Biomedical Sciences, Nagasaki University, Nagasaki, Japan.
  • Andrey Bychkov
    Department of Pathology, Nagasaki University Graduate School of Biomedical Sciences, Sakamoto, Nagasaki, Japan; Department of Pathology, Kameda Medical Center, Kamogawa, Chiba, Japan.
  • Junya Fukuoka
    Department of Pathology, Nagasaki University Graduate School of Biomedical Sciences, Sakamoto, Nagasaki, Japan; Department of Pathology, Kameda Medical Center, Kamogawa, Chiba, Japan. Electronic address: fukuokaj@nagasaki-u.ac.jp.

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