Dynamic Digital Twins for Adaptive Cancer Management: From Predictive Modeling to Personalized Therapy

Abstract

Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular diagnostics, targeted therapeutics, immunotherapy, and precision medicine. The biological complexity of cancer, together with continuous changes in tumor evolution, immune interactions, and therapeutic response, necessitates computational approaches capable of adapting alongside the patient's disease. Dynamic digital twins have emerged as a transformative paradigm in precision oncology by creating continuously evolving virtual representations of individual patients that integrate radiological imaging, digital pathology, genomic sequencing, transcriptomics, proteomics, metabolomics, laboratory biomarkers, wearable technologies, electronic health records, and longitudinal clinical data. Unlike conventional predictive models based on static datasets, dynamic digital twins continuously update their computational state as new patient information becomes available, enabling adaptive prediction of disease progression, therapeutic response, treatment-related toxicity, recurrence, and long-term clinical outcomes. Advances in artificial intelligence (AI), multimodal foundation models, transformer architectures, graph neural networks, reinforcement learning, agentic AI, digital health technologies, and cloud computing have significantly accelerated development of dynamic digital twin ecosystems capable of supporting diagnosis, prognostic prediction, treatment optimization, adaptive radiation therapy, immunotherapy, surgical planning, drug discovery, and clinical decision support. Despite remarkable technological progress, challenges remain regarding interoperability, computational scalability, explainability, cybersecurity, regulatory validation, and ethical governance. This review provides a comprehensive overview of dynamic digital twins in adaptive cancer management, highlighting computational foundations, current clinical applications, implementation challenges, and future perspectives for personalized oncology.[1]