A CT-Based Deep Learning Radiomics Nomogram to Predict Histological Grades of Head and Neck Squamous Cell Carcinoma.

Journal: Academic radiology
Published Date: Nov 30, 2022

Abstract

RATIONALE AND OBJECTIVES: Accurate pretreatment assessment of histological differentiation grade of head and neck squamous cell carcinoma (HNSCC) is crucial for prognosis evaluation. This study aimed to construct and validate a contrast-enhanced computed tomography (CECT)-based deep learning radiomics nomogram (DLRN) to predict histological differentiation grades of HNSCC.

Authors

  • Ying-Mei Zheng
    Health Management Center, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Jun-Yi Che
    Department of Radiology, Qingdao Municipal Hospital, Qingdao, China.
  • Ming-Gang Yuan
    Department of Nuclear Medicine, Affiliated Qingdao Central Hospital, Qingdao University, Qingdao, China.
  • Zeng-Jie Wu
    Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Jing Pang
    Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Rui-Zhi Zhou
    Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China.
  • Xiao-Li Li
  • Cheng Dong
    Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China. Electronic address: chengdong@qdu.edu.cn.

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