# Predictive Digital Twin for Optimizing Patient-Specific Radiotherapy Regimens under Uncertainty in High-Grade Gliomas

arXiv: https://arxiv.org/abs/2308.12429 · HTML: https://mlsanity.com/paper/2308.12429/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2023.

## Built on by (most cited first, 12 of 12)

- [TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model](https://mlsanity.com/paper/2404.01273/) (2024)
- [Personalized Predictions of Glioblastoma Infiltration: Mathematical Models, Physics-Informed Neural Networks and Multimodal Scans](https://mlsanity.com/paper/2311.16536/) (2023)
- [Bayesian BiLO: Bilevel Local Operator Learning for Efficient Uncertainty Quantification of Bayesian PDE Inverse Problems with Low-Rank Adaptation](https://mlsanity.com/paper/2507.17019/) (2025)
- [A Comprehensive Survey on Surgical Digital Twin](https://mlsanity.com/paper/2512.00019/) (2025)
- [AI-Augmented Adaptive Digital Twin Modeling for Brain Tumor Evolution Prediction and Treatment Scheduling](https://mlsanity.com/paper/2607.13877/) (2026)
- [A digital-twin framework for forecasting treatment-day imaging with contour uncertainty in adaptive proton radiotherapy](https://mlsanity.com/paper/2609.24725/) (2026)
- [A Digital Twin prototype for traffic sign recognition of a learning-enabled autonomous vehicle](https://mlsanity.com/paper/2402.09097/) (2024)
- [Anatomy-DT: A Cross-Diffusion Digital Twin for Anatomical Evolution](https://mlsanity.com/paper/2509.25280/) (2025)
- [SoC-DT: Standard-of-Care Aligned Digital Twins for Patient-Specific Tumor Dynamics](https://mlsanity.com/paper/2510.03287/) (2025)
- [Adaptive digital twins for predictive decision-making: Online Bayesian learning of transition dynamics](https://mlsanity.com/paper/2512.13919/) (2025)
- [FluidGaussian: Propagating Simulation-Based Uncertainty Toward Functionally-Intelligent 3D Reconstruction](https://mlsanity.com/paper/2603.21356/) (2026)
- [Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programming](https://mlsanity.com/paper/2606.06094/) (2026)

## Abstract (arXiv)

We develop a methodology to create data-driven predictive digital twins for optimal risk-aware clinical decision-making. We illustrate the methodology as an enabler for an anticipatory personalized treatment that accounts for uncertainties in the underlying tumor biology in high-grade gliomas, where heterogeneity in the response to standard-of-care (SOC) radiotherapy contributes to sub-optimal patient outcomes. The digital twin is initialized through prior distributions derived from population-level clinical data in the literature for a mechanistic model's parameters. Then the digital twin is personalized using Bayesian model calibration for assimilating patient-specific magnetic resonance imaging data and used to propose optimal radiotherapy treatment regimens by solving a multi-objective risk-based optimization under uncertainty problem. The solution leads to a suite of patient-specific optimal radiotherapy treatment regimens exhibiting varying levels of trade-off between the two competing clinical objectives: (i) maximizing tumor control (characterized by minimizing the risk of tumor volume growth) and (ii) minimizing the toxicity from radiotherapy. The proposed digital twin framework is illustrated by generating an in silico cohort of 100 patients with high-grade glioma growth and response properties typically observed in the literature. For the same total radiation dose as the SOC, the personalized treatment regimens lead to median increase in tumor time to progression of around six days. Alternatively, for the same level of tumor control as the SOC, the digital twin provides optimal treatment options that lead to a median reduction in radiation dose by 16.7% (10 Gy) compared to SOC total dose of 60 Gy. The range of optimal solutions also provide options with increased doses for patients with aggressive cancer, where SOC does not lead to sufficient tumor control.