Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas

Anirban Chaudhuri; Graham Pash; David A Hormuth 2nd; Guillermo Lorenzo; Michael Kapteyn; Chengyue Wu; Ernesto A B F Lima; Thomas E Yankeelov; Karen Willcox

doi:10.3389/frai.2023.1222612

Predictive digital twin for optimizing patient-specific radiotherapy regimens under uncertainty in high-grade gliomas

Front Artif Intell. 2023 Oct 11:6:1222612. doi: 10.3389/frai.2023.1222612. eCollection 2023.

Authors

Anirban Chaudhuri¹, Graham Pash¹, David A Hormuth 2nd^{1

2}, Guillermo Lorenzo^{1

3}, Michael Kapteyn¹, Chengyue Wu¹, Ernesto A B F Lima^{1

4}, Thomas E Yankeelov^{1

2

5

6

7

8}, Karen Willcox¹

Affiliations

¹ Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States.
² Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX, United States.
³ Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy.
⁴ Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX, United States.
⁵ Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States.
⁶ Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX, United States.
⁷ Department of Oncology, The University of Texas at Austin, Austin, TX, United States.
⁸ Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, United States.

Abstract

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. The calibrated digital twin is 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.

Keywords: adaptive radiotherapy; brain cancer; digital twin; mathematical oncology; personalized tumor forecasts; risk-aware clinical decision-making; uncertainty quantification.

Abstract

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