The efficacy of F-FDG-PET-based radiomic and deep-learning features using a machine-learning approach to predict the pathological risk subtypes of thymic epithelial tumors.

Journal: The British journal of radiology
Published Date: Jun 1, 2022

Abstract

OBJECTIVE: To examine whether the machine-learning approach using 18-fludeoxyglucose positron emission tomography (F-FDG-PET)-based radiomic and deep-learning features is useful for predicting the pathological risk subtypes of thymic epithelial tumors (TETs).

Authors

  • Masatoyo Nakajo
    Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan. toyo.nakajo@dolphin.ocn.ne.jp.
  • Aya Takeda
    Department of General Thoracic Surgery, Kagoshima University, Graduate School of Medical and Dental Sciences, Kagoshima, Japan.
  • Akie Katsuki
    Research and Development Department, GE Healthcare Japan, Tokyo, Japan.
  • Megumi Jinguji
    Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
  • Kazuyuki Ohmura
    Research and Development Department, GE Healthcare Japan, Tokyo, Japan.
  • Atsushi Tani
    Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
  • Masami Sato
    Department of General Thoracic Surgery, Kagoshima University, Graduate School of Medical and Dental Sciences, Kagoshima, Japan.
  • Takashi Yoshiura
    Department of Radiology, Kagoshima University, Graduate School of Medical and Dental Sciences, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.

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