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Background <p>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.</p> Objective <p>How is a&#xa0;medical digital twin defined, which data foundation is available in uro-oncology, and which clinical use cases can be envisaged?</p> Methods <p>This work comprises a narrative review including a&#xa0;description of the digital twin concept, a&#xa0;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).</p> Results <p>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.</p> Conclusion <p>Digital twins have the potential to enable a&#xa0;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.</p>

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„Digital twins“ in der Uroonkologie

  • 期刊论文

  • 发表日期：

    2026年4月22日

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  • Johanna Smielowski,
  • Maximilian Glienke,
  • Sherif Mehralivand,
  • Gregor Duwe,
  • Felix Chun,
  • Hendrik Borgmann,
  • Magdalena Görtz

摘要

Background

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.

Objective

How is a medical digital twin defined, which data foundation is available in uro-oncology, and which clinical use cases can be envisaged?

Methods

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).

Results

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.

Conclusion

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.