Robust and interpretable deep learning system for prognostic stratification of extranodal natural killer/T-cell lymphoma.

Journal: European journal of nuclear medicine and molecular imaging
Published Date: Dec 23, 2024

Abstract

PURPOSE: Extranodal natural killer/T-cell lymphoma (ENKTCL) is an hematologic malignancy with prognostic heterogeneity. We aimed to develop and validate DeepENKTCL, an interpretable deep learning prediction system for prognosis risk stratification in ENKTCL.

Authors

  • Chong Jiang
  • Zekun Jiang
    College of Computer Science, Sichuan University, Chengdu, Sichuan, China.
  • Xinyu Zhang
    Wenzhou Medical University Renji College, Wenzhou, Zhejiang, China.
  • Linhao Qu
    Digital Medical Research Center, School of Basic Medical Science, Fudan University, Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention, Shanghai 200032, People's Republic of China.
  • Kexue Fu
    Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China.
  • Yue Teng
    Haidian Maternal & Child Health Hospital Nutrition Clinic, Beijing 100080, China.
  • Ruihe Lai
    Department of Nuclear Medicine, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, China.
  • Rui Guo
    College of Chemistry&Chemical Engineering, Xiamen University, Xiamen 361005, China.
  • Chongyang Ding
    Department of Nuclear Medicine, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China.
  • Kang Li
    Department of Otolaryngology, Longgang Otolaryngology hospital & Shenzhen Key Laboratory of Otolaryngology, Shenzhen Institute of Otolaryngology, Shenzhen, Guangdong, China.
  • Rong Tian
    Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610041, China. Electronic address: rongtiannuclear@126.com.

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