The concept of the Virtual Human Twin (VHT) is emerging as a transformative paradigm in oncology, aiming to create patient-specific computational replicas capable of simulating tumor evolution and treatment response. Advances in medical imaging, computational modeling, artificial intelligence, and multi-omics integration now enable dynamic representations of tumor growth, regression, and resistance mechanisms. Despite these advances, most current models remain fragmented, retrospective, or insufficiently validated for clinical deployment. Solid tumors, characterized by spatial heterogeneity and therapy-induced morphological changes, particularly demand integrative and mechanistic modeling approaches. Bridging quantitative imaging, biological modeling, and clinical decision-making is therefore essential to move from proof-of-concept digital models to viable, clinically actionable virtual twins.
This Research Topic addresses the critical challenge of translating virtual twin concepts into viable, validated tools for oncology. While computational tumor models have significantly progressed—incorporating radiomics, AI-driven segmentation, mechanistic growth laws, and therapy-response simulations—their integration into coherent, clinically usable workflows remains limited.
We aim to foster contributions that advance the development of end-to-end VHT frameworks, from diagnostic imaging and data harmonization to predictive simulation and clinical validation. Key challenges include model personalization, uncertainty quantification, prospective validation, interoperability, regulatory compliance, and integration into hospital infrastructures.
Recent advances in longitudinal imaging analysis, mechanistic–AI hybrid modeling, digital biomarkers, and high-performance computing provide unprecedented opportunities to construct dynamic twins capable of simulating lesion inflation/deflation under therapy and predicting patient-specific trajectories.
This Research Topic seeks to consolidate these advances into a translational roadmap toward robust, validated, and scalable Virtual Human Twins that can meaningfully support precision oncology.
This Research Topic welcomes interdisciplinary contributions addressing the development, validation, and clinical translation of Virtual Twins in oncology.
Themes of interest include:
• Patient-specific tumor growth and response modeling
• Imaging-driven digital twins and radiomics integration
• Hybrid mechanistic–AI frameworks
• Multi-scale and multi-omics data integration
• Validation strategies (retrospective and prospective cohorts)
• Uncertainty quantification and model robustness
• Regulatory, ethical, and interoperability considerations
• Clinical workflow integration and decision-support systems
We welcome Original Research Articles, Methodological Papers, Clinical Validation Studies, Reviews, Perspectives, and Technology Reports that contribute to advancing viable, reproducible, and clinically actionable virtual twin ecosystems in cancer research.
Please note that manuscripts consisting solely of bioinformatics or computational analysis of public omics databases that are not supplemented by relevant functional validation (clinical cohort or biological validation in vitro or in vivo) are out of scope for this Research Topic.
Article types and fees
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Case Report
Clinical Trial
Editorial
FAIR² Data
General Commentary
Hypothesis and Theory
Methods
Mini Review
Opinion
Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.
Article types
This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:
Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
