Computational Models for Patient Stratification in Urologic Cancers – Creating Robust and Trustworthy Multimodal AI for Health Care
Current clinical approaches fail to fully utilize unstructured data in managing prostate cancer (PCa) and kidney cancer (KC), leading to inefficiencies in patient care and increased costs. Effective diagnostics and treatments depend on integrating multimodal data, yet progress is hampered by limited data accessibility and a lack of collaborative validation between clinicians and computer scientists. To address these challenges, the EU-funded COMFORT project aims to develop commercially viable, data-driven multimodal decision support systems. These systems will improve clinical prognostication, patient stratification, and personalized treatment while also assessing the trust that healthcare professionals and patients place in AI-driven tools.
Authors
- Lisa C. Adams (ORCID: https://orcid.org/0000-0001-5836-4542)
- Keno K. Bressem (ORCID: https://orcid.org/0000-0001-9249-8624)
- Lili Jiang (ORCID: https://orcid.org/0000-0002-7788-3986)
- Carol B. Meyer
- Jawed Nawabi (ORCID: https://orcid.org/0000-0002-1137-0643)
- Renato Cuocolo (ORCID: https://orcid.org/0000-0002-1452-1574)
- Antonis Billis (ORCID: https://orcid.org/0000-0002-1854-7560)
- Alessa Hering (ORCID: https://orcid.org/0000-0002-7602-803X)
- Alexander Löser (ORCID: https://orcid.org/0000-0002-4440-3261)
Institutions
- Berliner Hochschule für Technik (DE)
- University of Salerno (IT)
- Radboud University Nijmegen (NL)
- Aristotle University of Thessaloniki (GR)
- TUM Klinikum (DE)
- Radboud University Medical Center (NL)
- European Research and Project Office (DE)
- Deutsches Herzzentrum München (DE)
- Charité - Universitätsmedizin Berlin (DE)
- Umeå University (SE)
Publication Details
- Published
- 2025-06-18
- DOI
- https://doi.org/10.1109/cbms65348.2025.00034
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00