Machine learning-derived prognostic signature for progression-free survival in non-metastatic nasopharyngeal carcinoma.

Journal: Head & neck
PMID: 39077955

Abstract

BACKGROUND: Early detection of high-risk nasopharyngeal carcinoma (NPC) recurrence is essential. We created a machine learning-derived prognostic signature (MLDPS) by combining three machine learning (ML) models to predict progression-free survival (PFS) in patients with non-metastatic NPC.

Authors

  • Zhichao Zuo
    School of Mathematics and Computational Science, Xiangtan University, Xiangtan, Hunan, China.
  • Jie Ma
    Respiratory Department, Beijing Hospital of Integrated Traditional Chinese and Western Medicine, Beijing, China.
  • Mi Yan
    Department of Radiology, Xiangtan Central Hospital, Xiangtan, China.
  • Wu Ge
    Department of Radiology, Xiangtan Central Hospital, Xiangtan, China.
  • Ting Yao
    Institute of Intelligent Control and Robotics, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China.
  • Lu Zhou
    School of Environment, Tsinghua University Beijing 100084 P. R. China zhoulu@mail.tsinghua.edu.cn.
  • Ying Zeng
    Tongji University School of Medicine, Tongji University, Shanghai, China.
  • Yang Liu
    Department of Computer Science, Hong Kong Baptist University, Hong Kong, China.

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