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| Home > Publications database > Digital twins in uro-oncology. [„Digital twins“ in der Uroonkologie]. |
| Home > Publications database > Digital twins in uro-oncology. [„Digital twins“ in der Uroonkologie]. |
| Journal Article (Review Article) | DKFZ-2026-00968 |
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2026
Springer Medizin
New York]
Abstract: Uro-oncology is moving toward precision medicine, driven by high-dimensional longitudinal data from imaging, pathology, molecular profiling, and follow-up. However, clinical decision-making often relies on static risk scores that cannot fully capture individual disease dynamics. Digital twins aim to integrate multimodal patient data and to provide patient-specific, dynamically updated simulation models, thereby enabling 'what-if' testing of interventions.How is a medical digital twin defined, which data foundation is available in uro-oncology, and which clinical use cases can be envisaged?This work comprises a narrative review including a description of the digital twin concept, a structured presentation of multimodal data (laboratory parameters, imaging, pathology, omics, long-term outcomes), and an overview of representative published applications (e.g., tumor growth reconstruction, virtual pathology, surgical 3D twins).Current digital twin research in uro-oncology largely represents partial digital twins (e.g., tumor progression models, virtual assessment, patient-specific 3D surgical planning). Potential clinical value of digital twins includes dynamic risk stratification, individualized treatment planning, and adaptive follow-up strategies. Major limitations relate to data quality, interoperability, external validation, interpretability, data privacy, and regulatory requirements for clinical deployment.Digital twins have the potential to enable a new era of predictive precision medicine in uro-oncology. Progress toward clinically actionable digital twins requires multimodal architectures, rigorous monitoring, and seamless integration into clinical workflows under robust governance and regulatory frameworks.
Keyword(s): Artificial intelligence ; Multimodal data integration ; Precision medicine ; Risk stratification ; Tumor growth
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