An artificial intelligence model for the pathological diagnosis of invasion depth and histologic grade in bladder cancer.

Journal: Journal of translational medicine
Published Date: Jan 23, 2023

Abstract

BACKGROUND: Accurate pathological diagnosis of invasion depth and histologic grade is key for clinical management in patients with bladder cancer (BCa), but it is labour-intensive, experience-dependent and subject to interobserver variability. Here, we aimed to develop a pathological artificial intelligence diagnostic model (PAIDM) for BCa diagnosis.

Authors

  • Jiexin Pan
    Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107th Yanjiangxi Road, Guangzhou, China.
  • Guibin Hong
    Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107th Yanjiangxi Road, Guangzhou, China.
  • Hong Zeng
    School of Computer Science and Technology, Hangzhou Dianzi University, China.
  • Chengxiao Liao
    Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107th Yanjiangxi Road, Guangzhou, China.
  • Huarun Li
    Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107th Yanjiangxi Road, Guangzhou, China.
  • Yuhui Yao
    Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107th Yanjiangxi Road, Guangzhou, China.
  • Qinghua Gan
    Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107th Yanjiangxi Road, Guangzhou, China.
  • Yun Wang
    Department of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, People's Republic of China.
  • Shaoxu Wu
    Departments of Urology, Radiology, Emergency Medicine, and Respiratory Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
  • Tianxin Lin
    Departments of Urology, Radiology, Emergency Medicine, and Respiratory Medicine, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China. Electronic address: lintx@mail.sysu.edu.cn.

Keywords

External Resources

Popular Topics
Recent Journals