A multi-modal approach for decision making in bladder cancer

Bladder cancer remains a major global health challenge, characterized by diagnostic uncertainty, substantial treatment costs and high recurrence rates. Current diagnostic and treatment modalities, including cystoscopy, transurethral resection of bladder tumour and standard histopathology, have limitations, including the inability to detect flat lesions, frequent understaging and interobserver variability, highlighting a crucial need for improved approaches. Advances in artificial intelligence (AI), blue-light cystoscopy, narrow-band imaging, cytology and urinary markers show promise in enhancing early detection and diagnosis. Developments in multiparametric MRI, radiomics, genomics and AI-driven algorithms for histopathological analyses have demonstrated considerable improvements in staging and risk stratification of bladder tumours, enabling personalized therapy selection and prognostication. Despite these promising developments, challenges remain regarding standardization, external validation, cost-effectiveness and ethical considerations in clinical implementation. Future research should prioritize addressing these barriers through collaborative, multi-institutional studies and robust validation frameworks. Ultimately, adopting a comprehensive multimodal strategy, such as proposed, novel, multimodal decision-making frameworks in which these advances and technologies are integrated, promises to considerably advance precision oncology in bladder cancer, improving patient outcomes and reducing health care burdens.