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<p>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.</p>

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Digital Twin models to address long-term treatment toxicities in children and young adults with cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月6日

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  • Inas Elsayed,
  • Aleksandar Krstic,
  • Luis F. Iglesias-Martínez,
  • Jessica C. Ralston,
  • Walter Kolch

摘要

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.