Dose prediction via distance-guided deep learning: Initial development for nasopharyngeal carcinoma radiotherapy.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PMID: 35351537

Abstract

BACKGROUND AND PURPOSE: Geometric information such as distance information is essential for dose calculations in radiotherapy. However, state-of-the-art dose prediction methods use only binary masks without distance information. This study aims to develop a dose prediction deep learning method for nasopharyngeal carcinoma radiotherapy by taking advantage of the distance information as well as the mask information.

Authors

  • Meiyan Yue
    Shenzhen Institute of Advanced Technology, Chinese Academy of Science, Shenzhen, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China.
  • Xiaoguang Xue
    Department of Radiation Oncology, The Second Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Zhanyu Wang
    Department of Oncology, The Fourth Affiliated Hospital, Guangxi Medical University, Liuzhou, China.
  • Ricardo Lewis Lambo
    Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, China.
  • Wei Zhao
    Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu Province, P. R. China. lxy@jiangnan.edu.cn zhuye@jiangnan.edu.cn.
  • Yaoqin Xie
    Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China.
  • Jing Cai
    Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
  • Wenjian Qin
    Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.

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