A multi-modal approach for decision making in bladder cancer.
Authors
Affiliations (17)
Affiliations (17)
- School of Medicine and Biomedical Sciences, University of Oxford, Oxford, UK.
- Faculty of Medicine, Imperial College London, London, UK.
- School of Clinical Medicine, University of Cambridge, Cambridge, UK.
- University of Bristol Medical School, Bristol, UK.
- Department of Medical Oncology, Bartholomew's Hospital, Barts Health NHS Trust, London, UK.
- Portsmouth Hospitals University NHS Trust, Portsmouth, UK.
- Faculty of Science and Health, School of Pharmacy and Biomedical Sciences, University of Portsmouth, Portsmouth, UK.
- Institute of Nuclear Medicine, University College London, London, UK.
- London School of Hygiene & Tropical Medicine, London, UK.
- Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China.
- Department of Urology, Lister Hospital, ENHT Teaching NHS Trust, Stevenage, UK.
- Hertfordshire Medical School, University of Hertfordshire, Hatfield, UK.
- Department of Genitourinary Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- Houston Methodist Cancer Center, Houston, TX, USA.
- Department of Medical Oncology, Medway NHS Foundation Trust, Gillingham, UK.
- Faculty of Life Sciences and Medicine, School of Cancer & Pharmaceutical Sciences, King's College London, London, UK.
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK. [email protected].
Abstract
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.