Three-dimensional deep learning model complements existing models for preoperative disease-free survival prediction in localized clear cell renal cell carcinoma: a multicenter retrospective cohort study.

Journal: International journal of surgery (London, England)
PMID: 38896853

Abstract

BACKGROUND: Current prognostic models have limited predictive abilities for the growing number of localized (stage I-III) ccRCCs. It is, therefore, crucial to explore novel preoperative recurrence prediction models to accurately stratify patients and optimize clinical decisions. The purpose of this study was to develop and externally validate a computed tomography (CT)-based deep learning (DL) model for presurgical disease-free survival (DFS) prediction.

Authors

  • Yingjie Xv
    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.
  • Qing Jiang
    Department of Urology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Xuan Zhang
  • Yong Chen
    Department of Urology, Chongqing University Fuling Hospital, Chongqing, China.
  • Bangxin Xiao
    Department of Urology, The First Affiliated Hospital of Chongqing Medical University.
  • Siwen Yin
    Department of Urology, Chongqing University Fuling Hospital.
  • Zongyu Xia
    Department of Urology, Chongqing University Three Gorges Hospital.
  • Ming Qiu
    Department of Neurosurgery, South China Hospital of Shenzhen University, Shenzhen518111, P.R. China.
  • Yang Li
    Occupation of Chinese Center for Disease Control and Prevention, Beijing, China.
  • Hao Tan
    Department of Urology, The First Affiliated Hospital of Chongqing Medical University.
  • Mingzhao Xiao
    Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

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