Deep learning neural network of adenocarcinoma detection in effusion cytology.

Journal: American journal of clinical pathology
Published Date: Jul 30, 2025

Abstract

OBJECTIVE: Cytologic examination, which confirms the presence or absence of malignant cells, detects malignant cells from various organs, with adenocarcinoma as the most common histologic type. We developed a deep learning model to detect malignant cells in images obtained following effusion cytology.

Authors

  • Katsuhide Ikeda
    Pathophysiology Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan.
  • Nanako Sakabe
    Pathophysiology Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan.
  • Kenta Fukuda
    Pathophysiology Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan.
  • Shouichi Sato
    Clinical Engineering, Faculty of Medical Sciences, Juntendo University, Urayasu, Japan.
  • Toshiaki Hara
    Department of Medical Technique, Division of Pathology, Nagoya University Hospital, Nagoya, Aichi, Japan.
  • Harumi Kobayashi
    Department of Medical Technique, Division of Pathology, Nagoya University Hospital, Nagoya, Aichi, Japan.
  • Masato Nakaguro
    Department of Pathology and Laboratory Medicine, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8560, Japan.
  • Kennosuke Karube
    Department of Medical Technique, Division of Pathology, Nagoya University Hospital, Nagoya, Aichi, Japan.
  • Kohzo Nagata
    Pathophysiology Sciences, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan.

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