Machine-learning-based computed tomography radiomic analysis for histologic subtype classification of thymic epithelial tumours.

Journal: European journal of radiology
Published Date: May 1, 2020

Abstract

PURPOSE: To evaluate the performance of machine-learning-based computed tomography (CT) radiomic analysis to differentiate high-risk thymic epithelial tumours (TETs) from low-risk TETs according to the WHO classification.

Authors

  • Jianping Hu
    Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 ChaZhong Rd, Fuzhou, Fujian, 350005, PR China.
  • Yijing Zhao
    Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 ChaZhong Rd, Fuzhou, Fujian, 350005, PR China.
  • Mengcheng Li
    Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 ChaZhong Rd, Fuzhou, Fujian, 350005, PR China.
  • Yin Liu
    School of Chemistry and Chemical Engineering, Shandong University, Jinan, China.
  • Feng Wang
    Department of Oncology, Binzhou Medical University Hospital, Binzhou, Shandong, China.
  • Qiang Weng
    Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 ChaZhong Rd, Fuzhou, Fujian, 350005, PR China.
  • Ruixiong You
    Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 ChaZhong Rd, Fuzhou, Fujian, 350005, PR China.
  • Dairong Cao
    Department of Radiology, The First Affiliated Hospital of Fujian Medical University, 20 ChaZhong Rd, Fuzhou, Fujian, 350005, PR China. Electronic address: dairongcao@163.com.

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