AI predicting recurrence in non-muscle-invasive bladder cancer: systematic review with study strengths and weaknesses.

Journal: Frontiers in oncology
Published Date: Jan 7, 2025

Abstract

BACKGROUND: Non-muscle-invasive Bladder Cancer (NMIBC) is notorious for its high recurrence rate of 70-80%, imposing a significant human burden and making it one of the costliest cancers to manage. Current prediction tools for NMIBC recurrence rely on scoring systems that often overestimate risk and lack accuracy. Machine learning (ML) and artificial intelligence (AI) are transforming oncological urology by leveraging molecular and clinical data to enhance predictive precision.

Authors

  • Saram Abbas
    School of Engineering, Newcastle University, Newcastle Upon Tyne, United Kingdom.
  • Rishad Shafik
    School of Engineering, Newcastle University, Newcastle Upon Tyne, United Kingdom.
  • Naeem Soomro
    Department of Urology, Freeman Hospital, Newcastle upon Tyne, United Kingdom.
  • Rakesh Heer
    Division of Surgery, Imperial College London, London, United Kingdom.
  • Kabita Adhikari
    School of Engineering, Newcastle University, Newcastle Upon Tyne, United Kingdom.

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