Deep learning approach of diffusion-weighted imaging as an outcome predictor in laryngeal and hypopharyngeal cancer patients with radiotherapy-related curative treatment: a preliminary study.

Journal: European radiology
PMID: 35201406

Abstract

OBJECTIVES: This preliminary study aimed to develop a deep learning (DL) model using diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps to predict local recurrence and 2-year progression-free survival (PFS) in laryngeal and hypopharyngeal cancer patients treated with various forms of radiotherapy-related curative therapy.

Authors

  • Hayato Tomita
    Department of Radiology, Graduate School of Medical Science, University of the Ryukyus, Nishihara, Japan.
  • Tatsuaki Kobayashi
    School of Allied Health Sciences, Kitasato University.
  • Eichi Takaya
    Graduate School of Science and Technology, Keio University.
  • Sono Mishiro
    Department of AI Research Lab, Harada Academy, 2-54-4, Higashitaniyama, Kagoshima, Kagoshima, 891-0113, Japan.
  • Daisuke Hirahara
  • Atsuko Fujikawa
    Department of Radiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan.
  • Yoshiko Kurihara
    Department of Radiology, Machida Municipal Hospital, 2-15-41 Asahi-cho, Machida, Tokyo, 194-0023, Japan.
  • Hidefumi Mimura
    Department of Radiology, St. Marianna University School of Medicine, Kawasaki, Japan.
  • Yasuyuki Kobayashi

Keywords

External Resources

Popular Topics
Recent Journals