Deep learning-based radiomic nomogram to predict risk categorization of thymic epithelial tumors: A multicenter study.

Journal: European journal of radiology
Published Date: Oct 5, 2023

Abstract

PURPOSE: The study was aimed to develop and evaluate a deep learning-based radiomics to predict the histological risk categorization of thymic epithelial tumors (TETs), which can be highly informative for patient treatment planning and prognostic assessment.

Authors

  • Hao Zhou
    State Key Laboratory of Environment Health (Incubation), Key Laboratory of Environment and Health, Ministry of Education, Key Laboratory of Environment and Health (Wuhan), Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, #13 Hangkong Road, Wuhan, Hubei 430030, China.
  • Harrison X Bai
    Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
  • Zhicheng Jiao
  • Biqi Cui
    Department of Neurology, Xiangya Hospital, Central South University, Changsha 410008, China.
  • Jing Wu
    School of Pharmaceutical Science, Jiangnan University, Wuxi, 214122, Jiangsu, China.
  • Haijun Zheng
    Department of Radiology, First People's Hospital of Chenzhou, University of South China, Chenzhou 423000, China.
  • Huan Yang
  • Weihua Liao
    Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China.

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