From Algorithms to Outcomes: Reviewing AI's Role in Non-Muscle-Invasive Bladder Cancer Recurrence Prediction

• URL: http://arxiv.org/abs/2403.10586v1
• Date: Fri, 15 Mar 2024 17:03:45 GMT
• Title: From Algorithms to Outcomes: Reviewing AI's Role in Non-Muscle-Invasive Bladder Cancer Recurrence Prediction
• Authors: Saram Abbas, Dr Rishad Shafik, Prof Naeem Soomro, Prof Rakesh Heer, Dr Kabita Adhikari,
• Abstract summary: Bladder cancer, the leading urinary tract cancer, is responsible for 15 deaths daily in the UK. Current tools for predicting recurrence use scoring systems that overestimate risk and have poor accuracy. Machine learning (ML) techniques have emerged as a promising approach for predicting NMIBC recurrence.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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