Diagnostic performance of a deep-learning model using F-FDG PET/CT for evaluating recurrence after radiation therapy in patients with lung cancer.

Journal: Annals of nuclear medicine
Published Date: Apr 8, 2024

Abstract

OBJECTIVE: We developed a deep learning model for distinguishing radiation therapy (RT)-related changes and tumour recurrence in patients with lung cancer who underwent RT, and evaluated its performance.

Authors

  • Changhwan Sung
    Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Korea.
  • Jungsu S Oh
    Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.
  • Byung Soo Park
    Department of Nuclear Medicine, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-Ro 43-Gil, Songpa-Gu, Seoul, 05505, Korea.
  • Su Ssan Kim
    Department of Radiation Oncology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Si Yeol Song
    Department of Radiation Oncology, Asan Medical Center, Seoul, Republic of Korea.
  • Jong Jin Lee
    Division of Nuclear Medicine and Molecular Imaging, Department of Radiology, Stanford University, 300 Pasteur Dr, Stanford, CA, 94305, USA.

Keywords

External Resources

Popular Topics
Recent Journals