Machine Learning Model for Predicting Pheochromocytomas/Paragangliomas Surgery Difficulty: A Retrospective Cohort Study.

Journal: Annals of surgical oncology
Published Date:

Abstract

OBJECTIVE: We aimed to develop a machine learning (ML) model to preoperatively predict surgical difficulty for pheochromocytomas and paragangliomas (PPGLs) using clinical and radiomic features.

Authors

  • Yubing Zhang
    Department of Urology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China.
  • Qikun Guo
    Department of Interventional Radiology, The First Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, People's Republic of China.
  • Shurong Li
    Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China.
  • Zhiqiang Zhang
    Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
  • Fangzheng Xiang
    Department of Urology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China.
  • Wenhui Su
    Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China.
  • Yukun Wu
    Department of Urology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China.
  • Jiajie Yu
    Department of Andrology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China.
  • Yun Xie
    Department of Urology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China. xiey236@mail.sysu.edu.cn.
  • Cheng Luo
    Department of Cardiology, Liuzhou Workers' Hospital, The Fourth Affiliated Hospital of Guangxi Medical University, Liuzhou, China.
  • Fufu Zheng
    Department of Urology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China. zhengfuf@mail.sysu.edu.cn.