Digital Twin Ecosystems for Personalized Oncology: From Virtual Patients to Real-Time Clinical Intelligence

Abstract

Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular biology, targeted therapeutics, immunotherapy, and precision medicine. The extraordinary biological heterogeneity of tumors, coupled with dynamic interactions among malignant cells, the immune system, and the tumor microenvironment, continues to challenge conventional treatment strategies. Recent advances in artificial intelligence (AI), computational biology, systems medicine, cloud computing, and multimodal biomedical data integration have introduced digital twin ecosystems as a transformative framework for personalized oncology. Unlike conventional digital twins that primarily represent individual virtual patients, digital twin ecosystems integrate interconnected computational models encompassing radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, metabolomics, laboratory biomarkers, wearable technologies, electronic health records, hospital information systems, and real-time clinical data streams. These intelligent ecosystems continuously synchronize virtual and biological patients, enabling dynamic prediction of disease progression, therapeutic response, toxicity, recurrence, and long-term clinical outcomes. Advances in machine learning, deep learning, multimodal foundation models, graph neural networks, reinforcement learning, agentic AI, generative AI, and cloud-native computing have accelerated development of real-time digital oncology ecosystems capable of supporting diagnosis, molecular characterization, adaptive therapeutics, immunotherapy optimization, radiation planning, surgical decision-making, clinical trial matching, and survivorship care. Despite substantial progress, important challenges remain regarding interoperability, computational scalability, explainability, cybersecurity, ethical governance, regulatory validation, and equitable implementation. This review presents a comprehensive overview of digital twin ecosystems in oncology, emphasizing computational foundations, clinical applications, technological innovations, and future perspectives for real-time intelligent cancer care.