A CT-based deep learning model predicts overall survival in patients with muscle invasive bladder cancer after radical cystectomy: a multicenter retrospective cohort study.

Journal: International journal of surgery (London, England)
Published Date: May 1, 2024

Abstract

BACKGROUND: Muscle invasive bladder cancer (MIBC) has a poor prognosis even after radical cystectomy (RC). Postoperative survival stratification based on radiomics and deep learning (DL) algorithms may be useful for treatment decision-making and follow-up management. This study was aimed to develop and validate a DL model based on preoperative computed tomography (CT) for predicting postcystectomy overall survival (OS) in patients with MIBC.

Authors

  • Zongjie Wei
    Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Yingjie Xv
    Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Huayun Liu
    Department of Urology.
  • Yang Li
    Occupation of Chinese Center for Disease Control and Prevention, Beijing, China.
  • Siwen Yin
    Department of Urology, Chongqing University Fuling Hospital.
  • Yongpeng Xie
    Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Yong Chen
    Department of Urology, Chongqing University Fuling Hospital, Chongqing, China.
  • Fajin Lv
    Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Qing Jiang
    Department of Urology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Feng Li
    Department of General Surgery, Shanghai Traditional Chinese Medicine (TCM)-INTEGRATED Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China.
  • Mingzhao Xiao
    Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

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