Using machine learning for predicting cancer-specific mortality in bladder cancer patients undergoing radical cystectomy: a SEER-based study.

Journal: BMC cancer
PMID: 40119324

Abstract

BACKGROUND: Accurately assessing the prognosis of bladder cancer patients after radical cystectomy has important clinical and research implications. Current models, based on traditional statistical approaches and complex variables, have limited performance. We aimed to develop a machine learning (ML)-based prognostic model to predict 5-year cancer-specific mortality (CSM) in bladder cancer patients undergoing radical cystectomy, and compare its performance with current validated models.

Authors

  • Lei Dai
    School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China.
  • Kun Ye
    Department of Urology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China.
  • Gaosheng Yao
    Department of Urology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China.
  • Juan Lin
    Fujian Key Laboratory of Marine Enzyme Engineering, Fuzhou University Fuzhou, China.
  • Zhiping Tan
    Department of Urology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China.
  • Jinhuan Wei
    Department of Urology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China.
  • Yanchang Hu
    Sun Yat-sen University School of Medicine, Guangzhou, 510080, China.
  • Junhang Luo
    Department of Urology, The First Affiliated Hospital, Sun Yat-Sen University, 510080, Guangzhou, China. luojunh@mail.sysu.edu.cn.
  • Yong Fang
    College of Food Science and Engineering, Nanjing University of Finance and Economics/Collaborative Innovation Center for Modern Grain Circulation and Safety, Nanjing 210023, People's Republic of China.
  • Wei Chen
    Department of Urology, Zigong Fourth People's Hospital, Sichuan, China.

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