Toward Actionable Digital Twins for Radiation-Based Imaging and Therapy: Mathematical Formulation, Modular Workflow, and an OpenKBP-Based Dose-Surrogate Prototype

Abstract

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.

Figures (4)

• Figure 1: Actionable digital twin architecture for radiation-based imaging and therapy. Measurements update PatientData, calibration updates model state or parameters, the solver produces predictive distributions with uncertainty, and the decision module selects constrained actions for the clinical system.
• Figure 2: Representative axial slice for OpenKBP case pt_289: reference dose, predicted mean dose $\mu(\mathbf{x})$, absolute error $|\mu(\mathbf{x})-d(\mathbf{x})|$, and voxel-wise epistemic uncertainty $\sigma(\mathbf{x})$ estimated by Monte Carlo dropout.
• Figure 3: Uncertainty-aware DVHs for case pt_289. Solid curves denote reference DVHs, dashed curves denote predictive-mean DVHs, and shaded regions indicate pointwise 95% predictive intervals from the Monte Carlo dropout ensemble.
• Figure 4: Illustrative proxy virtual-therapy adaptation trajectory. Top: surrogate TCP and NTCP objectives across 30 fractions with a simulated anatomical shift at fraction 10. Bottom: corresponding evolution of the epistemic uncertainty summary $\sigma(\mathbf{x})$. The example demonstrates the response of the calibration and decision modules in a proxy experiment; it is not intended as a validated estimate of clinical outcome probabilities.