Prediction of visceral pleural invasion of clinical stage I lung adenocarcinoma using thoracoscopic images and deep learning.

Journal: Surgery today
PMID: 37864054

Abstract

PURPOSE: To develop deep learning models using thoracoscopic images to identify visceral pleural invasion (VPI) in patients with clinical stage I lung adenocarcinoma, and to verify if these models can be applied clinically.

Authors

  • Yoshifumi Shimada
  • Toshihiro Ojima
    Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan.
  • Yutaka Takaoka
    Data Science Center for Medicine and Hospital Management, Toyama University Hospital, 2630 Sugitani, Toyama, Japan.
  • Aki Sugano
    Data Science Center for Medicine and Hospital Management, Toyama University Hospital, 2630 Sugitani, Toyama, Japan.
  • Yoshiaki Someya
    Center for Data Science and Artificial Intelligence Research Promotion, Toyama University Hospital, 2630 Sugitani, Toyama, Japan.
  • Kenichi Hirabayashi
    Department of Diagnostic Pathology, Faculty of Medicine Academic Assembly, University of Toyama Toyama Japan.
  • Takahiro Homma
    Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan.
  • Naoya Kitamura
    Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan.
  • Yushi Akemoto
    Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan.
  • Keitaro Tanabe
    Department of Thoracic Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan.
  • Fumitaka Sato
    Department of Microbiology, Kindai University Faculty of Medicine, Osakasayama, Osaka 589-8511, Japan.
  • Naoki Yoshimura
    Department of Cardiovascular Surgery, University of Toyama, 2630 Sugitani, Toyama, Japan.
  • Tomoshi Tsuchiya
    Department of Surgical Oncology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.

Keywords

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