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.

翻译：非肌层浸润性膀胱癌（NMIBC）以其70-80%的高复发率而著称，不仅给患者带来沉重负担，也是治疗成本最高的癌症之一。当前用于预测NMIBC复发的工具主要依赖评分系统，这些系统往往高估风险且准确性有限。基于机器学习（ML）的技术通过整合分子与临床数据，为NMIBC复发预测提供了具有前景的新途径。本综述论文系统评析了基于机器学习的NMIBC复发预测框架，重点关注其统计稳健性与算法效能。我们通过聚焦不同预测任务、数据模态与机器学习模型，细致剖析了各项研究的优势与不足，在揭示其显著性能的同时也指出固有局限性。综合利用影像组学、临床、组织病理学及基因组学等多模态数据的各类机器学习算法，在精准预测NMIBC复发方面展现出巨大潜力。然而，这些技术要实现广泛应用仍需应对模型泛化性与可解释性等挑战，这凸显了加强跨领域合作、构建高质量数据集以及纳入成本效益分析的必要性。我们通过细致的分类与深入分析，阐明了影响这些人工智能技术在实际临床中推进与应用的关键细节、复杂因素及实施情境。这项严谨的评估研究使研究者能更深入理解所用机器学习算法的内在机理，相关见解有助于优化研究方法、突破现有局限、提升模型泛化能力，最终为降低医疗成本、改善患者预后提供支撑。