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A multi-modal approach for decision making in bladder cancer

Nature Reviews Urology (2026)Cite this article

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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.

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Fig. 1: The Vesical Imaging-Reporting and Data System for assessing muscle invasion in bladder cancer.
Fig. 2: Clinical decision-making algorithm showing how emerging technology can be used at each stage of detecting and investigating bladder cancer.
Fig. 3: Clinical decision-making algorithm showing how emerging technology can be integrated into each stage of treatment planning and staging for non-muscle-invasive bladder cancer.
Fig. 4: Clinical decision-making algorithm explaining how emerging technology can be integrated into each stage of treatment planning and staging for muscle-invasive bladder cancer.
Fig. 5: Clinical decision-making algorithm for personalized management of metastatic urothelial carcinoma.

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Authors and Affiliations

  1. School of Medicine and Biomedical Sciences, University of Oxford, Oxford, UK

    Hasan Al-Sattar

  2. Faculty of Medicine, Imperial College London, London, UK

    Hao Ding

  3. School of Clinical Medicine, University of Cambridge, Cambridge, UK

    Oluchi Okoli

  4. University of Bristol Medical School, Bristol, UK

    Somto Okoli

  5. Department of Medical Oncology, Bartholomew’s Hospital, Barts Health NHS Trust, London, UK

    Aruni Ghose & Athar Haroon

  6. Portsmouth Hospitals University NHS Trust, Portsmouth, UK

    Giuseppe Luigi Banna

  7. Faculty of Science and Health, School of Pharmacy and Biomedical Sciences, University of Portsmouth, Portsmouth, UK

    Giuseppe Luigi Banna

  8. Institute of Nuclear Medicine, University College London, London, UK

    Simon Wan

  9. London School of Hygiene & Tropical Medicine, London, UK

    Jonathan Wong

  10. Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China

    Jeremy Teoh

  11. Department of Urology, Lister Hospital, ENHT Teaching NHS Trust, Stevenage, UK

    Nikhil Vasdev

  12. Hertfordshire Medical School, University of Hertfordshire, Hatfield, UK

    Nikhil Vasdev

  13. Department of Genitourinary Medical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Eleni Efstathiou

  14. Houston Methodist Cancer Center, Houston, TX, USA

    Eleni Efstathiou

  15. Department of Medical Oncology, Medway NHS Foundation Trust, Gillingham, UK

    Stergios Boussios

  16. Faculty of Life Sciences and Medicine, School of Cancer & Pharmaceutical Sciences, King’s College London, London, UK

    Stergios Boussios

  17. School of Biomedical Engineering and Imaging Sciences, King’s College London, London, UK

    Sola Adeleke

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Contributions

H.A.-S., H.D., O.O., A.G., G.L.B., S.W., J.W., J.T., N.V. and S.A. researched data for the article. H.A.-S., H.D., O.O., S.O., G.L.B., J.T. and S.A. contributed substantially to discussion of the content. H.A.-S., H.D., O.O., S.O., A.G., G.L.B., J.W., J.T. and S.A. wrote the article H.A.-S., H.D., S.O., A.G., G.L.B., S.W., A.H., J.W., J.T. N.V., E.E. S.B. and S.A. reviewed and/or edited the manuscript before submission.

Corresponding author

Correspondence to Sola Adeleke.

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Competing interests

G.L.B. reports receiving personal fees from advisory boards for Accord, AstraZeneca, Amgen and Merck; and from speaker bureaus for Astellas, AstraZeneca, Amgen, Bayer, Merck, Novartis and Pfizer. G.L.B. holds four patents with ST Microelectronics and has received travel and accommodation support for scientific conferences from Accord, Merck and Janssen. J.T. reports receiving honoraria for lectures from Astellas, Boston Scientific, Combat Medical, Ferring, Ipsen, Janssen, Olympus and Sanofi; consultancy or advisory board fees from Astellas, Aulea Medical, CMR Robotics, Combat Medical, EQT, Ferring, Illumicell AI, Janssen, MRI PRO, MedTech Syndicates, Merck Sharp & Dohme, Phase Scientific and Procept BioRobotics; and research grant support from Baxter, Bristol-Myers Squibb, Ferring, Janssen, Merck Sharp & Dohme, Karl Storz and Olympus. The other authors declare no competing interests.

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Al-Sattar, H., Ding, H., Okoli, O. et al. A multi-modal approach for decision making in bladder cancer. Nat Rev Urol (2026). https://doi.org/10.1038/s41585-025-01122-7

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