Radiomics-based machine learning and deep learning to predict serosal involvement in gallbladder cancer.

Journal: Abdominal radiology (New York)
Published Date: Oct 3, 2023

Abstract

OBJECTIVE: Our study aimed to determine whether radiomics models based on contrast-enhanced computed tomography (CECT) have considerable ability to predict serosal involvement in gallbladder cancer (GBC) patients.

Authors

  • Shengnan Zhou
    Department of Gastrointestinal Surgery, China-Japan Friendship Hospital, Beijing, China.
  • Shaoqi Han
    General Surgery Department, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China.
  • Weijie Chen
  • Xuesong Bai
    Digestive endoscopy center, Dazhou Central Hospital, Dazhou 635000, China.
  • Weidong Pan
    Radiology Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, China Academy of Medical Science & Peking Union Medical College, Beijing, China.
  • Xianlin Han
    General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, China Academy of Medical Science & Peking Union Medical College, No. 1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China. hanxianlin@pumch.cn.
  • Xiaodong He
    General Surgery Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, China Academy of Medical Science & Peking Union Medical College, No. 1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China. Hexdpumch@sina.com.

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