CT-Based Radiomics Analysis of Different Machine Learning Models for Discriminating the Risk Stratification of Pheochromocytoma and Paraganglioma: A Multicenter Study.

Journal: Academic radiology
PMID:

Abstract

RATIONALE AND OBJECTIVES: Using different machine learning models CT-based radiomics to integrate clinical radiological features to discriminating the risk stratification of pheochromocytoma/paragangliomas (PPGLs).

Authors

  • Yongjie Zhou
    Department of Radiology, Jiangxi Cancer Hospital, Nanchang, China; The Second Affiliated Hospital of Nanchang Medical College, Nanchang, China; Jiangxi Clinical Research Center for Cancer, Nanchang, China.
  • Yuan Zhan
    Department of Pathology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
  • Jinhong Zhao
    Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
  • Linhua Zhong
    Department of Radiology, Jiangxi Cancer Hospital, Nanchang, China.
  • Yongming Tan
    Department of Radiology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
  • Wei Zeng
    State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral and Maxillofacial Surgery, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
  • Qiao Zeng
    Jingtai Technology Co. Ltd Floor 4, No. 9, Yifenghua Industrial Zone, 91 Huaning Road, Longhua District Shenzhen Guangdong Province 518109 China.
  • Mingxian Gong
    Department of Radiology, Jiangxi Cancer Hospital, Nanchang, China.
  • Aihua Li
    Department of Radiology, Jiangxi Cancer Hospital, Nanchang, China.
  • Lianggeng Gong
    Department of Radiology, the Second Affiliated Hospital of Nanchang University, Nanchang, China.
  • Lan Liu
    School of Statistics, University of Minnesota at Twin Cities.