Weakly supervised deep learning for determining the prognostic value of F-FDG PET/CT in extranodal natural killer/T cell lymphoma, nasal type.

Journal: European journal of nuclear medicine and molecular imaging
PMID: 33611614

Abstract

PURPOSE: To develop a weakly supervised deep learning (WSDL) method that could utilize incomplete/missing survival data to predict the prognosis of extranodal natural killer/T cell lymphoma, nasal type (ENKTL) based on pretreatment F-FDG PET/CT results.

Authors

  • Rui Guo
    College of Chemistry&Chemical Engineering, Xiamen University, Xiamen 361005, China.
  • Xiaobin Hu
  • Haoming Song
    Department of Informatics, Technical University of Munich, Munich, Germany.
  • Pengpeng Xu
    State Key Laboratory of Medical Genomics, Shanghai Institute of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Haoping Xu
    Department of Radiation, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Axel Rominger
  • Xiaozhu Lin
    Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Bjoern Menze
  • Biao Li
    Key Laboratory of Renewable Energy, Guangdong Key Laboratory of New and Renewable Energy Research and Development, Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China.
  • Kuangyu Shi
    Universitätsklinik für Nuklearmedizin, Inselspital University Hospital Bern, University of Bern, Bern, Switzerland.

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