Nasopharyngeal cancer adaptive radiotherapy with CBCT-derived synthetic CT: deep learning-based auto-segmentation precision and dose calculation consistency on a C-Arm linac.

Journal: Radiation oncology (London, England)
Published Date: Aug 28, 2025

Abstract

BACKGROUND: To evaluate the precision of automated segmentation facilitated by deep learning (DL) and dose calculation in adaptive radiotherapy (ART) for nasopharyngeal cancer (NPC), leveraging synthetic CT (sCT) images derived from cone-beam CT (CBCT) scans on a conventional C-arm linac.

Authors

  • Weijie Lei
    Department of Radiation Oncology, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, China. weijie_lei@163.com.
  • Lixiang Han
    Department of Radiation Oncology, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
  • Zhenmei Cao
    Department of Radiation Oncology, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
  • Tingting Duan
    Department of Radiation Oncology, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
  • Bin Wang
    State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China; New South Wales Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga 2650, Australia. Electronic address: bin.a.wang@dpi.nsw.gov.au.
  • Caihong Li
    * Department of Computer Science and Engineering, Shandong University of Technology, Shandong 255049, P. R. China.
  • Xi Pei
    Department of Engineering and Applied Physics, School of Physics, University of Science and Technology of China, Hefei, Anhui, China.

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