Computed tomography and radiation dose images-based deep-learning model for predicting radiation pneumonitis in lung cancer patients after radiation therapy.

Journal: Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PMID: 36842666

Abstract

PURPOSE: To develop a deep learning model that combines CT and radiation dose (RD) images to predict the occurrence of radiation pneumonitis (RP) in lung cancer patients who received radical (chemo)radiotherapy.

Authors

  • Zhen Zhang
    School of Pharmacy, Jining Medical University, Rizhao, Shandong, China.
  • Zhixiang Wang
    Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
  • Tianchen Luo
    Institute of System Science, National University of Singapore, 119260, Singapore.
  • Meng Yan
    School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
  • Andre Dekker
    Department of Radiation Oncology (MAASTRO Clinic), Dr. Tanslaan 12, Maastricht, The Netherlands.
  • Dirk de Ruysscher
    a Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology , Maastricht University Medical Centre , Maastricht , The Netherlands.
  • Alberto Traverso
    Maastricht University Medical Centre, Netherlands.
  • Leonard Wee
    Maastricht University Medical Centre, Netherlands.
  • Lujun Zhao
    Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, 300060, China. Electronic address: zhaolujun@tjmuch.com.

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