Preoperatively predicting survival outcome for clinical stage IA pure-solid non-small cell lung cancer by radiomics-based machine learning.

Journal: The Journal of thoracic and cardiovascular surgery
PMID: 38788833

Abstract

OBJECTIVE: Clinical stage IA non-small cell lung cancer (NSCLC) showing a pure-solid appearance on computed tomography is associated with a worse prognosis. This study aimed to develop and validate machine-learning models using preoperative clinical and radiomic features to predict overall survival (OS) in clinical stage IA pure-solid NSCLC.

Authors

  • Haoji Yan
    Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan.
  • Takahiro Niimi
    Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan.
  • Takeshi Matsunaga
    Department of General Thoracic Surgery, Juntendo University School of Medicine, 1-3 Hongo 3-chome, Bunkyo-ku, Tokyo, 113-8431, Japan.
  • Mariko Fukui
    Department of General Thoracic Surgery, Juntendo University School of Medicine, 1-3 Hongo 3-chome, Bunkyo-ku, Tokyo, 113-8431, Japan.
  • Aritoshi Hattori
    Department of General Thoracic Surgery, Juntendo University School of Medicine, 1-3, Hongo 3-chome, Bunkyo-ku, Tokyo, 113-8431, Japan.
  • Kazuya Takamochi
    Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan. Electronic address: ktakamo@juntendo.ac.jp.
  • Kenji Suzuki
    Department of General Thoracic Surgery, Juntendo University School of Medicine, Tokyo, Japan.

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