Digital twins for radiation-based imaging and therapy are most useful when they assimilate patient data, quantify predictive uncertainty, and support clinically constrained decisions. This paper presents a modular framework for actionable digital twins in radiation-based imaging and therapy and instantiates its reproducible open-data component using the \openkbpfull{} benchmark. The framework couples PatientData, Model, Solver, Calibration, and Decision modules and formalizes latent-state updating, uncertainty propagation, and chance-constrained action selection. As an initial implementation, we build a GPU-ready PyTorch/MONAI reimplementation of the \openkbp{} starter pipeline: an 11-channel, 19.2M-parameter 3D U-Net trained with a masked loss over the feasible region and equipped with Monte Carlo dropout for voxel-wise epistemic uncertainty. To emulate the update loop on a static benchmark, we introduce decoder-only proxy recalibration and illustrate uncertainty-aware virtual-therapy evaluation using DVH-based and biological utilities. A complete three-fraction loop including recalibration, Monte Carlo inference, and spatial optimization executes in 10.3~s. On the 100-patient test set, the model achieved mean dose and DVH scores of 2.65 and 1.82~Gy, respectively, with 0.58~s mean inference time per patient. The \openkbp{} case study thus serves as a reproducible test bed for dose prediction, uncertainty propagation, and proxy closed-loop adaptation, while future institutional studies will address longitudinal calibration with delivered-dose logs and repeat imaging.

翻译：放射成像与治疗的数字孪生最有效的应用场景在于其能够同化患者数据、量化预测不确定性并支持临床约束下的决策。本文提出了一种面向放射成像与治疗的可执行数字孪生模块化框架，并利用OpenKBP全称基准实例化了其可复现的开源数据组件。该框架整合了患者数据、模型、求解器、校准与决策模块，形式化了隐状态更新、不确定性传播及机会约束下的行动选择。作为初步实现，我们基于OpenKBP初始流程构建了支持GPU的PyTorch/MONAI重构版本：一个11通道、1920万参数的三维U-Net，采用可行域上的掩码损失进行训练，并配备蒙特卡洛dropout以实现体素级认知不确定性。为在静态基准上模拟更新循环，我们引入了仅解码器的代理重新校准，并展示了基于DVH和生物学效用的不确定性感知虚拟治疗评估。包含重新校准、蒙特卡洛推理与空间优化的完整三分数循环可在10.3秒内执行。在100例患者测试集上，模型实现的平均剂量和DVH评分分别为2.65 Gy和1.82 Gy，每例患者平均推理时间为0.58秒。因此，OpenKBP案例研究可作为剂量预测、不确定性传播及代理闭环自适应的可复现测试平台，而未来机构级研究将涉及基于实际剂量日志与重复成像的纵向校准。