Digital twins in oncology: From predictive modelling to personalised treatment strategies

The digital twin (DT) concept, originating from engineering disciplines, has emerged as a transformative technology in healthcare, particularly in oncology. A digital twin creates a dynamic, virtual replica of a patient's physiological and pathological state, integrating multi-dimensional data to enable personalised cancer care. Despite growing interest, comprehensive reviews examining the breadth of DT applications in oncology remain limited. This narrative review aims to synthesise current evidence on digital twin applications in oncology, evaluate their potential to transform cancer care delivery, and identify challenges hindering clinical translation. A comprehensive literature search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore databases from inception to September 2025. Studies describing DT development, validation, or application in any cancer type were included. Grey literature, conference proceedings, and expert commentaries were also reviewed to capture emerging trends. Digital twins demonstrate applications across the cancer care continuum, including precision treatment selection, radiotherapy optimisation, drug development, immuno-oncology modelling, surgical planning, and survivorship care. Integration of multi-omics data, imaging biomarkers, and artificial intelligence enables dynamic simulation of tumour behaviour and treatment response. However, challenges persist in data integration, model validation, computational scalability, and ethical governance. Digital twin technology holds substantial promise for advancing precision oncology through predictive, personalised, and adaptive care strategies. Addressing current limitations through interdisciplinary collaboration and regulatory framework development is essential for clinical implementation. • Digital twins create dynamic virtual replicas of patients for personalised care • Applications span treatment selection, radiotherapy, drug development and surgery • Multi-omics data integration enables tumour behaviour and response simulation • Challenges include data integration, model validation, and scalability issues • AI enhances predictive capabilities through imaging and mathematical modelling