Digital Twins in Cancer Care: Building Virtual Patients for Personalized Medicine

Abstract

Cancer care is undergoing a major transformation through the convergence of artificial intelligence (AI), computational modeling, systems biology, and precision medicine. Among the most promising innovations is the development of digital twins—continuously evolving virtual representations of individual patients that integrate clinical, radiological, pathological, genomic, molecular, physiological, and longitudinal health information to simulate disease progression and therapeutic response. Unlike conventional predictive models that analyze isolated datasets, digital twins continuously incorporate newly acquired patient information, allowing personalized prediction, adaptive treatment optimization, toxicity assessment, and long-term survivorship planning. Recent advances in machine learning, deep learning, multimodal learning, transformer architectures, graph neural networks, foundation models, and generative AI have significantly enhanced the construction and clinical applicability of oncology digital twins. These intelligent computational systems are increasingly being investigated for tumor diagnosis, radiogenomics, computational pathology, immunotherapy prediction, adaptive radiation planning, surgical simulation, drug discovery, and clinical decision support. Furthermore, integration with wearable technologies, cloud computing, digital pathology, and electronic health records enables continuous updating of virtual patient models throughout the cancer journey. Despite remarkable progress, significant challenges remain regarding data standardization, interoperability, computational complexity, explainability, cybersecurity, ethical governance, regulatory validation, and clinical implementation. This review provides a comprehensive overview of digital twin technologies in cancer care, emphasizing their computational foundations, clinical applications, emerging innovations, and future role in advancing personalized medicine.[1]