Abstract
Although treatment of childhood and young adult cancer has enormously progressed, long-term treatment-related toxicities (LLCTT) prevent survivors from leading a healthy life. Predictive markers are essential for identifying LLCTT early enough to enable personalised therapies that minimise risks. The complexity of LLCTT poses challenges in developing predictive markers using conventional approaches. Here, we provide an overview of how an innovative strategy, Digital Twins, harnesses recent advances in computational modelling to predict and eventually manage treatment toxicities via a personalised approach. We also address the challenges that must be overcome to integrate these models into paediatric cancer care effectively.
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Acknowledgements
This work was supported by Research Ireland and National Children's Research Centre/Children’s Health Ireland through the Precision Oncology Ireland grant 18/SPP/3522. I.E. was supported by H2020-MSCA-COFUND-2019 No. 945425 "DevelopMed". The funders had no part in the writing of or influence on the contents of this paper. During the preparation of this work the author(s) used ChatGPT 4.0 and Gemini 2.5 in order to search for peer-reviewed papers that report computational models or digital twin models of toxicities associated with treatment for paediatric cancer. After using these tools, the authors reviewed all publications used in the manuscript and take full responsibility for the content of the published article.
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Elsayed, I., Krstic, A., Iglesias-Martínez, L.F. et al. Digital Twin models to address long-term treatment toxicities in children and young adults with cancer. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02656-9
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DOI: https://doi.org/10.1038/s41746-026-02656-9
