Prediction of prognosis in lung cancer using machine learning with inter-institutional generalizability: A multicenter cohort study (WJOG15121L: REAL-WIND).

Journal: Lung cancer (Amsterdam, Netherlands)
Published Date: Jul 18, 2024

Abstract

OBJECTIVES: Predicting the prognosis of lung cancer is crucial for providing optimal medical care. However, a method to accurately predict the overall prognosis in patients with stage IV lung cancer, even with the use of machine learning, has not been established. Moreover, the inter-institutional generalizability of such algorithms remains unexplored. This study aimed to establish machine learning-based algorithms with inter-institutional generalizability to predict prognosis.

Authors

  • Daichi Fujimoto
    Internal Medicine III, Wakayama Medical University, Wakayama, Japan.
  • Hidetoshi Hayashi
    Department of Medical Oncology, Kindai University Faculty of Medicine, Osaka, Japan. Electronic address: hidet31@med.kindai.ac.jp.
  • Kenta Murotani
    Biostatistics Center, Kurume University, Fukuoka, Japan.
  • Yukihiro Toi
    Department of Pulmonary Medicine, Sendai Kousei Hospital, Sendai, Japan.
  • Toshihide Yokoyama
    Department of Respiratory Medicine, Kurashiki Central Hospital, Kurashiki, Japan.
  • Terufumi Kato
    Department of Thoracic Oncology, Kanagawa Cancer Center, Yokohama, Japan.
  • Teppei Yamaguchi
    Department of Thoracic Oncology, Aichi Cancer Center Hospital, Nagoya, Japan.
  • Kaoru Tanaka
    Department of Medical Oncology, Kindai University Faculty of Medicine, Osaka, Japan.
  • Satoru Miura
    Department of Internal Medicine, Niigata Cancer Center Hospital, Niigata, Japan.
  • Motohiro Tamiya
    Department of Thoracic Oncology, Osaka International Cancer Institute, Osaka, Japan.
  • Motoko Tachihara
    Division of Respiratory Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, Kobe, Japan.
  • Takehito Shukuya
    Department of Respiratory Medicine, Juntendo University, Graduate School of Medicine, Tokyo, Japan.
  • Yuko Tsuchiya-Kawano
    Department of Respiratory Medicine, Kitakyushu Municipal Medical Center, Kitakyushu, Japan.
  • Yuki Sato
    Systems and Information Engineering Master's Program in Computer Science, University of Tsukuba, 1-1-1 Tenoudai, Tsukuba City, Ibaraki, 305-0821, Japan. s2220599@u.tsukuba.ac.jp.
  • Satoshi Ikeda
    Department of Respiratory Medicine, Kanagawa Cardiovascular and Respiratory Center, Yokohama, Japan.
  • Shinya Sakata
    Department of Respiratory Medicine, Kumamoto University Hospital, Kumamoto, Japan.
  • Takeshi Masuda
    Department of Molecular and Internal Medicine.
  • Shinnosuke Takemoto
    Department of Respiratory Medicine, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan.
  • Kohei Otsubo
    Department of Respiratory Medicine, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
  • Ryota Shibaki
    Internal Medicine III, Wakayama Medical University, Wakayama, Japan.
  • Miki Makino
    NTT Data Corp., Res. & Dev. Headquarters, Tokyo, Japan.
  • Isamu Okamoto
    Research Institute for Diseases of the Chest, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
  • Nobuyuki Yamamoto
    Department of Pulmonary Medicine and Medical Oncology, Wakayama Medical University, Wakayama, Japan.

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