Multi-task deep learning-based radiomic nomogram for prognostic prediction in locoregionally advanced nasopharyngeal carcinoma.

Journal: European journal of nuclear medicine and molecular imaging
PMID: 37596343

Abstract

PURPOSE: Prognostic prediction is crucial to guide individual treatment for locoregionally advanced nasopharyngeal carcinoma (LA-NPC) patients. Recently, multi-task deep learning was explored for joint prognostic prediction and tumor segmentation in various cancers, resulting in promising performance. This study aims to evaluate the clinical value of multi-task deep learning for prognostic prediction in LA-NPC patients.

Authors

  • Bingxin Gu
  • Mingyuan Meng
  • Mingzhen Xu
    Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, People's Republic of China.
  • David Dagan Feng
  • Lei Bi
  • Jinman Kim
    School of Information Technologies, University of Sydney, Australia; Institute of Biomedical Engineering and Technology, University of Sydney, Australia. Electronic address: jinman.kim@sydney.edu.au.
  • Shaoli Song
    Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, 200032, China. shaoli-song@163.com.

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