A deep learning MRI-based signature may provide risk-stratification strategies for nasopharyngeal carcinoma.

Journal: European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
PMID: 37358652

Abstract

OBJECTIVE: As the prognosis of nasopharyngeal carcinoma (NPC) is influenced by various factors, making it difficult for clinical physicians to predict the outcome, the objective of this study was to develop a deep learning-based signature for risk stratification in NPC patients.

Authors

  • Chen Yang
    Department of Diagnostic Ultrasound Imaging & Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.
  • Yuan Chen
    Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY 10032.
  • Luchao Zhu
    Department of Radiation Oncology, Xiamen Cancer Center, Xiamen Key Laboratory of Radiation Oncology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361003, Fujian, China.
  • Liansheng Wang
    Department of Computer Science, Xiamen University, Xiamen 361005, China.
  • Qin Lin
    Art College of Guizhou University of Finance and Economics, Guiyang 550001, Guizhou, China.

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