Digital Twin Architectures for Personalized Cancer Imaging, Disease Progression Modeling, and Precision Oncology

Abstract

Digital twin technology is emerging as a transformative paradigm in precision oncology by enabling the creation of dynamic, patient-specific virtual representations that continuously integrate multimodal imaging, molecular profiles, pathological findings, and longitudinal clinical data. Originally developed for the aerospace and manufacturing industries, digital twins have rapidly evolved into a promising healthcare technology capable of simulating disease progression, predicting treatment outcomes, and supporting individualized clinical decision-making. In oncology, digital twin frameworks combine advanced medical imaging, radiomics, artificial intelligence (AI), machine learning, computational modeling, and mechanistic biological simulations to generate comprehensive virtual models that accurately reflect tumor behavior and patient-specific disease evolution. Recent advances have expanded the application of digital twins across the cancer care continuum, including tumor detection and segmentation, disease progression modeling, treatment response prediction, radiotherapy planning, surgical guidance, immunotherapy optimization, and longitudinal monitoring of therapeutic outcomes. The integration of multimodal imaging with genomics, pathology, and electronic health records is further enhancing the accuracy and clinical utility of these predictive models, enabling increasingly personalized approaches to cancer management. Emerging technologies such as deep learning, foundation models, federated learning, and multi-omics integration are expected to further accelerate the development of clinically deployable digital twin ecosystems. Despite their considerable promise, several challenges continue to limit widespread clinical implementation, including limited data interoperability, lack of standardized data acquisition and validation protocols, concerns regarding model interpretability and reproducibility, computational complexity, regulatory uncertainty, and ethical issues related to privacy and data governance. Addressing these challenges will be essential for translating digital twin technologies from research environments into routine clinical practice. This review provides a comprehensive overview of digital twin architectures for personalized cancer imaging and disease progression modeling, summarizes current applications across major malignancies, discusses enabling technologies and methodological frameworks, critically evaluates existing limitations, and highlights future directions toward intelligent, continuously learning digital twin ecosystems for precision oncology. The convergence of digital twins, artificial intelligence, multimodal imaging, and computational oncology has the potential to fundamentally transform personalized cancer diagnosis, therapeutic planning, response assessment, and long-term disease management.