Early prediction of distant metastasis in patients with uterine cervical cancer treated with definitive chemoradiotherapy by deep learning using pretreatment [ 18 F]fluorodeoxyglucose positron emission tomography/computed tomography.

Journal: Nuclear medicine communications
PMID: 38165173

Abstract

OBJECTIVES: A deep learning (DL) model using image data from pretreatment [ 18 F]fluorodeoxyglucose ([ 18 F] FDG)-PET or computed tomography (CT) augmented with a novel imaging augmentation approach was developed for the early prediction of distant metastases in patients with locally advanced uterine cervical cancer.

Authors

  • Kuo-Chen Wu
    Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan.
  • Shang-Wen Chen
    Department of Radiation Oncology, China Medical University Hospital, Taichung, Taiwan.
  • Te-Chun Hsieh
    Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung, Taiwan.
  • Kuo-Yang Yen
    Department of Nuclear Medicine and PET Center, China Medical University Hospital, Taichung, Taiwan.
  • Chao-Jen Chang
    Artificial Intelligence Center, China Medical University Hospital.
  • Yu-Chieh Kuo
    Artificial Intelligence Center, China Medical University Hospital.
  • Ruey-Feng Chang
    Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan and Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan.
  • Kao Chia-Hung
    Artificial Intelligence Center, China Medical University Hospital.

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