  Bladder cancer, the leading urinary tract cancer, is responsible for 15
deaths daily in the UK. This cancer predominantly manifests as
non-muscle-invasive bladder cancer (NMIBC), characterised by tumours not yet
penetrating the muscle layer of the bladder wall. NMIBC is plagued by a very
high recurrence rate of 70-80% and hence the costliest treatments. Current
tools for predicting recurrence use scoring systems that overestimate risk and
have poor accuracy. Inaccurate and delayed prediction of recurrence
significantly elevates the likelihood of mortality. Accurate prediction of
recurrence is hence vital for cost-effective management and treatment planning.
This is where Machine learning (ML) techniques have emerged as a promising
approach for predicting NMIBC recurrence by leveraging molecular and clinical
data. This review provides a comprehensive analysis of ML approaches for
predicting NMIBC recurrence. Our systematic evaluation demonstrates the
potential of diverse ML algorithms and markers, including radiomic, clinical,
histopathological, genomic, and biochemical data in enhancing recurrence
prediction and personalised patient management. We summarise various prediction
tasks, data modalities, and ML models used, highlighting their performance,
limitations, and future directions of incorporating cost-effectiveness.
Challenges related to generalisability and interpretability of artificial
intelligent models are discussed, emphasising the need for collaborative
efforts and robust datasets.
