Deep learning-based tumour segmentation and total metabolic tumour volume prediction in the prognosis of diffuse large B-cell lymphoma patients in 3D FDG-PET images.

Journal: European radiology
PMID:

Abstract

OBJECTIVES: To demonstrate the effectiveness of automatic segmentation of diffuse large B-cell lymphoma (DLBCL) in 3D FDG-PET scans using a deep learning approach and validate its value in prognosis in an external validation cohort.

Authors

  • Chong Jiang
  • Kai Chen
    Department of Critical Care Medicine, Fujian Provincial Key Laboratory of Critical Care Medicine, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fujian Provincial Center for Critical Care Medicine, Fuzhou, Fujian, China.
  • Yue Teng
    Haidian Maternal & Child Health Hospital Nutrition Clinic, Beijing 100080, China.
  • Chongyang Ding
    Department of Nuclear Medicine, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China.
  • Zhengyang Zhou
    Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou 215123, China; School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China.
  • Yang Gao
    State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100050, China.
  • Junhua Wu
    National Institute of Healthcare Data Science at Nanjing University, Nanjing, China.
  • Jian He
    School of Software Engineering, Beijing University of Technology, Beijing, China. Electronic address: jianhee@bjut.edu.cn.
  • Kelei He
  • Junfeng Zhang
    Medcine College of Pingdingshan University, Pingdingshan 476000, China.