Deep learning feature-based model for predicting lymphovascular invasion in urothelial carcinoma of bladder using CT images.

Journal: Insights into imaging
Published Date: May 18, 2025

Abstract

OBJECTIVES: Lymphovascular invasion significantly impacts the prognosis of urothelial carcinoma of the bladder. Traditional lymphovascular invasion detection methods are time-consuming and costly. This study aims to develop a deep learning-based model to preoperatively predict lymphovascular invasion status in urothelial carcinoma of bladder using CT images.

Authors

  • Bangxin Xiao
    Department of Urology, The First Affiliated Hospital of Chongqing Medical University.
  • Yang Lv
  • Canjie Peng
    Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Zongjie Wei
    Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Qiao Xv
    Department of Urology, Yongchuan Hospital of Chongqing Medical University, 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.
  • Huayun Liu
    Department of Urology.
  • Feng Li
    Department of General Surgery, Shanghai Traditional Chinese Medicine (TCM)-INTEGRATED Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China.
  • Yingjie Xv
    Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Quanhao He
    Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Mingzhao Xiao
    Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Keywords

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