Dose prediction of CyberKnife Monte Carlo plan for lung cancer patients based on deep learning: robust learning of variable beam configurations.

Journal: Radiation oncology (London, England)
PMID: 39587661

Abstract

BACKGROUND: Accurate calculation of lung cancer dose using the Monte Carlo (MC) algorithm in CyberKnife (CK) is essential for precise planning. We aim to employ deep learning to directly predict the 3D dose distribution calculated by the MC algorithm, enabling rapid and accurate automatic planning. However, most current methods solely focus on conventional intensity-modulated radiation therapy and assume a consistent beam configuration across all patients. This study seeks to develop a more versatile model incorporating variable beam configurations of CK and considering the patient's anatomy.

Authors

  • Yuchao Miao
    National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Jiwei Li
  • Ruigang Ge
    Department of Radiation Oncology, People's Liberation Army General Hospital, Beijing 100853, P.R.China.
  • Chuanbin Xie
    Department of Radiation Oncology, The First Medical Center of the People's Liberation Army General Hospital, Beijing, China.
  • Yaoying Liu
    School of Physics, Beihang University, Beijing, 102206, China.
  • Gaolong Zhang
    School of Physics, Beihang University, Beijing, People's Republic of China.
  • Mingchang Miao
    Department of Radiation Oncology, the Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
  • Shouping Xu
    Department of Radiotherapy, First Medical Center of PLA General Hospital, BeiJing 100853, P.R.China.

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