A CT-based Deep Learning Radiomics Nomogram for the Prediction of EGFR Mutation Status in Head and Neck Squamous Cell Carcinoma.

Journal: Academic radiology
Published Date: Jul 21, 2023

Abstract

RATIONALE AND OBJECTIVES: Accurately assessing epidermal growth factor receptor (EGFR) mutation status in head and neck squamous cell carcinoma (HNSCC) patients is crucial for prognosis and treatment selection. This study aimed to construct and validate a contrast-enhanced computed tomography (CECT)-based deep learning radiomics nomogram (DLRN) to predict EGFR mutation status of HNSCC.

Authors

  • Ying-Mei Zheng
    Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Jing Pang
    Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Zong-Jing Liu
    Department of Pediatric Hematology, The Affiliated Hospital of Qingdao University, Qingdao, China (Z.-j.L.).
  • Ming-Gang Yuan
    Department of Nuclear Medicine, Affiliated Qingdao Central Hospital, Qingdao University, Qingdao, China.
  • Jie Li
    Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence Application Technology Research Institute, Shenzhen Polytechnic University, Shenzhen, China.
  • Zeng-Jie Wu
    Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Yan Jiang
    Department of Nursing/Evidence-based Nursing Center, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
  • Cheng Dong
    Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China. Electronic address: chengdong@qdu.edu.cn.

Keywords

External Resources

Popular Topics
Recent Journals