Construction of an enhanced computed tomography radiomics model for non-invasively predicting granzyme A in head and neck squamous cell carcinoma by machine learning.

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: 36920557

Abstract

PURPOSE: Classical prognostic indicators of head and neck squamous cell carcinoma (HNSCC) can no longer meet the clinical needs of precision medicine. This study aimed to establish a radiomics model to predict Granzyme A (GZMA) expression in patients with HNSCC.

Authors

  • Ren Hang
    Department of Stomatology, Wuxi Second People's Hospital, No. 68, Zhongshan Road, Wuxi, 214001, China.
  • Guo Bai
    Department of Oral Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, No. 639, Zhi-Zao-Ju Road, Shanghai, 200011, China.
  • Bin Sun
    Department of Urology, General Hospital of the Air Force, PLA, No. 30 Fucheng Road Haidian District, Beijing, 100142 China.
  • Peng Xu
    Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
  • Xiaofeng Sun
    Department of Stomatology, Wuxi Second People's Hospital, No. 68, Zhongshan Road, Wuxi, 214001, China.
  • Guoxin Yan
    Department of Stomatology, Wuxi Second People's Hospital, No. 68, Zhongshan Road, Wuxi, 214001, China.
  • Wenhao Zhang
    Aliyun School of Big Data, Changzhou University, 213164 Changzhou, China.
  • Fang Wang
    Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education and Hunan Key Laboratory for Computation and Simulation in Science and Engineering, Xiangtan University, Xiangtan, China.

Keywords

External Resources

Popular Topics
Recent Journals