放射性药物治疗 (RPT) 是核医学的一个快速发展的领域，多种 RPT 已在多种不同类型癌症的治疗中得到很好的应用。然而，目前的 RPT 方法通常遵循一种有点不灵活的“一刀切”范式，即无论患者的个人特征和特征如何，每个周期都会对患者施用相同量的放射性。这种方法未能考虑患者间放射性药代动力学、放射生物学和免疫学因素的差异，这些差异可能会显着影响治疗结果。为了解决这一限制，我们建议开发治疗诊断数字双胞胎（TDT），以根据实际患者数据个性化 RPT。我们提出的路线图概述了创建和完善 TDT 所需的步骤，这些 TDT 可以优化肿瘤的辐射剂量，同时最大限度地减少对危险器官的毒性。 TDT 模型结合了基于生理学的放射性药代动力学 (PBRPK) 模型，该模型还与放射生物学优化器和免疫调节器相关，同时考虑了影响 RPT 反应的因素。通过使用 TDT 模型，我们设想能够进行虚拟临床试验，选择治疗方法以改善治疗结果，同时最大限度地减少与副作用相关的风险。该框架可以使从业者最终能够为亚组和个体患者开发量身定制的 RPT 解决方案，从而提高治疗的精确度、准确性和疗效，同时最大限度地降低患者的风险。通过将 TDT 模型纳入 RPT，我们可以为癌症治疗的精准医学新时代铺平道路。© 作者。

Radiopharmaceutical therapy (RPT) is a rapidly developing field of nuclear medicine, with several RPTs already well established in the treatment of several different types of cancers. However, the current approaches to RPTs often follow a somewhat inflexible "one size fits all" paradigm, where patients are administered the same amount of radioactivity per cycle regardless of their individual characteristics and features. This approach fails to consider inter-patient variations in radiopharmacokinetics, radiation biology, and immunological factors, which can significantly impact treatment outcomes. To address this limitation, we propose the development of theranostic digital twins (TDTs) to personalize RPTs based on actual patient data. Our proposed roadmap outlines the steps needed to create and refine TDTs that can optimize radiation dose to tumors while minimizing toxicity to organs at risk. The TDT models incorporate physiologically-based radiopharmacokinetic (PBRPK) models, which are additionally linked to a radiobiological optimizer and an immunological modulator, taking into account factors that influence RPT response. By using TDT models, we envisage the ability to perform virtual clinical trials, selecting therapies towards improved treatment outcomes while minimizing risks associated with secondary effects. This framework could empower practitioners to ultimately develop tailored RPT solutions for subgroups and individual patients, thus improving the precision, accuracy, and efficacy of treatments while minimizing risks to patients. By incorporating TDT models into RPTs, we can pave the way for a new era of precision medicine in cancer treatment.© The author(s).