Reviewing AI's Role in Non-Muscle-Invasive Bladder Cancer Recurrence Prediction

• URL: http://arxiv.org/abs/2403.10586v2
• Date: Mon, 16 Sep 2024 14:19:39 GMT
• Title: Reviewing AI's Role in Non-Muscle-Invasive Bladder Cancer Recurrence Prediction
• Authors: Saram Abbas, Rishad Shafik, Naeem Soomro, Rakesh Heer, Kabita Adhikari,
• Abstract summary: Non-muscle-invasive Bladder Cancer (NMIBC) imposes a significant human burden and is one of the costliest cancers to manage. Current tools for predicting NMIBC recurrence rely on scoring systems that often overestimate risk and have poor accuracy. Machine learning (ML)-based techniques have emerged as a promising approach for predicting NMIBC recurrence by leveraging molecular and clinical data.
• Score: 0.4369058206183195
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Notorious for its 70-80% recurrence rate, Non-muscle-invasive Bladder Cancer (NMIBC) imposes a significant human burden and is one of the costliest cancers to manage. Current tools for predicting NMIBC recurrence rely on scoring systems that often overestimate risk and have poor accuracy. This is where Machine learning (ML)-based techniques have emerged as a promising approach for predicting NMIBC recurrence by leveraging molecular and clinical data. This comprehensive review paper critically analyses ML-based frameworks for predicting NMIBC recurrence, focusing on their statistical robustness and algorithmic efficacy. We meticulously examine the strengths and weaknesses of each study, by focusing on various prediction tasks, data modalities, and ML models, highlighting their remarkable performance alongside inherent limitations. A diverse array of ML algorithms that leverage multimodal data spanning radiomics, clinical, histopathological, and genomic data, exhibit significant promise in accurately predicting NMIBC recurrence. However, the path to widespread adoption faces challenges concerning the generalisability and interpretability of models, emphasising the need for collaborative efforts, robust datasets, and the incorporation of cost-effectiveness. Our detailed categorisation and in-depth analysis illuminate the nuances, complexities, and contexts that influence real-world advancement and adoption of these AI-based techniques. This rigorous analysis equips researchers with a deeper understanding of the intricacies of the ML algorithms employed. Researchers can use these insights to refine approaches, address limitations, and boost generalisability of their ML models, ultimately leading to reduced healthcare costs and improved patient outcomes.

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