Abstract
Oncology digital twins are patient-specific computational models that are built by combining electronic health records, multiomics genomic data, and diagnostic imaging to simulate individual tumor biology and predict multiple treatment-related outcomes. Conceptually originated from aerospace engineering, it has matured clinically through convergent advances in radiomics, mechanistic tumor modeling, pharmacokinetic- pharmacodynamic systems, federated machine learning, and, most recently, large language model (LLM)-based clinical interfaces and agentic artificial intelligence (AI). For a practicing radiologist, digital twins offer a transformative role: imaging-derived quantitative features serve as the primary data, positioning diagnostic imaging at the center of these personalized oncology workflows. Key clinical applications especially in oncology span from chemotherapy response prediction, immunotherapy patient selection, personalized radiation planning, tumor progression modeling, and treatment toxicity forecasting. Several of these applications are achievable with current technology without any significant infrastructure investment. Substantial challenges include imaging data standards, absence of prospective validation, algorithmic bias in underrepresented populations, and regulatory uncertainty for continuously self-updating AI. This narrative review provides radiologists with a balanced, comprehensive overview of digital twin architecture, advanced enabling technologies, current clinical evidence, a practical roadmap for implementation, and a candid appraisal of barriers to adoption.