Virtual Oncology: Digital Twins for Predicting Cancer Evolution, Therapeutic Response, and Clinical Outcomes

Abstract

Cancer remains one of the leading causes of morbidity and mortality worldwide despite significant advances in molecular diagnostics, targeted therapeutics, immunotherapy, and precision medicine. The remarkable biological heterogeneity of tumors, together with dynamic interactions among malignant cells, the immune system, and the tumor microenvironment, continues to limit accurate prediction of disease progression and individualized therapeutic response. Recent advances in artificial intelligence (AI), computational biology, systems medicine, mathematical modeling, and multimodal biomedical data integration have introduced virtual oncology through digital twin technology as a transformative framework for predictive precision medicine. A digital twin is a continuously evolving virtual representation of an individual patient that integrates radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, metabolomics, laboratory biomarkers, wearable technologies, electronic health records, and longitudinal clinical information to simulate disease evolution and therapeutic outcomes. Unlike conventional predictive models that analyze isolated datasets, digital twins continuously synchronize with real-world patient data, enabling dynamic prediction of tumor progression, treatment efficacy, toxicity, recurrence, metastatic dissemination, and long-term clinical outcomes. Advances in machine learning, deep learning, multimodal foundation models, graph neural networks, reinforcement learning, agentic AI, and generative artificial intelligence have substantially accelerated development of virtual oncology ecosystems capable of supporting diagnosis, prognostic prediction, adaptive therapeutics, immunotherapy optimization, radiation planning, drug discovery, and clinical decision support. Despite remarkable progress, important challenges remain regarding interoperability, computational scalability, explainability, cybersecurity, ethical governance, regulatory validation, and equitable clinical implementation.This review provides a comprehensive overview of virtual oncology based on digital twin technology, emphasizing computational foundations, clinical applications, emerging innovations, and future perspectives for predicting cancer evolution and optimizing personalized clinical care.