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

2026-03-26

AI Summary

This work proposes the first actionable digital twin framework tailored for radiotherapy, designed to integrate multimodal patient data, quantify prediction uncertainty, and support treatment decisions under clinical constraints. The framework comprises five core modules—PatientData, Model, Solver, Calibration, and Decision—and formalizes mechanisms for latent state updates, uncertainty propagation, and chance-constrained action selection, augmented with surrogate recalibration for closed-loop simulation. A GPU-accelerated 3D U-Net (11-channel) incorporating Monte Carlo Dropout, mask-aware loss, and rapid spatial optimization serves as the dose surrogate model trained on OpenKBP. Experimental results demonstrate an average dose error of 2.65 Gy and DVH score of 1.82 Gy across 100 test cases, with single-case inference requiring only 0.58 seconds and the full three-stage closed-loop pipeline completing in 10.3 seconds.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Reasoning under Uncertainty: Uncertainty Representations

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Web applications in cross-disciplinary domains and verticals such as mixed reality, smart cities, and digital health

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.

Problem

• Digital Twins
• Radiation Therapy
• Predictive Uncertainty
• Clinical Decision Support
• Dose Prediction

Innovation

• actionable digital twin
• uncertainty quantification
• Monte Carlo dropout
• modular framework
• dose prediction