A Fully Automated Artificial Intelligence-Based Approach to Predict Renal Function After Radical or Partial Nephrectomy.

Journal: Urology
Published Date: Feb 4, 2025

Abstract

OBJECTIVE: To test if our artificial intelligence (AI)-postoperative glomerular filtration rate (GFR) prediction is as accurate as a validated clinical model. The American Urologic Association recommends estimating postoperative GFR in patients with renal masses and prioritizing partial nephrectomy (PN) when GFR would be <45 ml/minutes/1.73 m if radical nephrectomy (RN) was performed. Previously validated models have limited clinical uptake.

Authors

  • Nour Abdallah
    University of Connecticut, Hartford, CT, USA.
  • Nityam Rathi
    Glickman Urological and Kidney Institute, Cleveland, OH. Electronic address: RATHIN@ccf.org.
  • Nicholas Heller
    Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, Minnesota.
  • Andrew Wood
    Oxford Health NHS Foundation Trust, Oxford, Oxfordshire, UK.
  • Rebecca Campbell
    Glickman Urological and Kidney Institute, Cleveland, OH.
  • Tarik Benidir
    Glickman Urological and Kidney Institute, Cleveland, OH.
  • Fabian Isensee
  • Resha Tejpaul
    Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, USA.
  • Chalairat Suk-Ouichai
    Department of Surgery, Siriraj Hospital, Mahidol University, Bangkok, Thailand.
  • Diego Aguilar Palacios
    Glickman Urological and Kidney Institute, Cleveland, OH. Electronic address: aguilad@ccf.org.
  • Alex You
    Case Western Reserve University, Cleveland, OH.
  • Satish Viswanath
    Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH. Electronic address: satish.viswanath@case.edu.
  • Brennan Flannery
    Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH. Electronic address: bxf169@case.edu.
  • Jihad Kaouk
    Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH. Electronic address: kaoukj@ccf.org.
  • Samuel Haywood
    Glickman Urological and Kidney Institute, Cleveland, OH.
  • Venkatesh Krishnamurthi
    Glickman Urological and Kidney Institute, Cleveland, OH.
  • Nikolaos Papanikolopoulos
    Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, USA.
  • Joseph Zabell
    Department of Urology, University of Minnesota Medical School, Minneapolis, MN. Electronic address: zabe0034@umn.edu.
  • Robert Abouassaly
    Glickman Urologic and Kidney Institute, Cleveland Clinic, Cleveland, Ohio, USA.
  • Erick M Remer
    Glickman Urological and Kidney Institute, Cleveland, OH; Department of Diagnostic Radiology, Imaging Institute, Cleveland Clinic, Cleveland, OH.
  • Steven Campbell
    Glickman Urological and Kidney Institute, Cleveland, OH.
  • Christopher J Weight
    Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, Ohio.

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