SoC-DT: Standard-of-Care Aligned Digital Twins for Patient-Specific Tumor Dynamics

• URL: http://arxiv.org/abs/2510.03287v1
• Date: Mon, 29 Sep 2025 04:14:11 GMT
• Title: SoC-DT: Standard-of-Care Aligned Digital Twins for Patient-Specific Tumor Dynamics
• Authors: Moinak Bhattacharya, Gagandeep Singh, Prateek Prasanna,
• Abstract summary: Accurate prediction of tumor trajectories under standard-of-care (SoC) therapies remains a major unmet need in oncology.<n>We introduce Standard-of-Care Digital Twin (SoC-DT), a differentiable framework that unifies reaction-diffusion tumor growth models.<n>An implicit-explicit exponential time-differencing solver, IMEX-SoC, is also proposed, which ensures stability, positivity, and scalability in SoC treatment situations.
• Score: 13.83306392535935
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate prediction of tumor trajectories under standard-of-care (SoC) therapies remains a major unmet need in oncology. This capability is essential for optimizing treatment planning and anticipating disease progression. Conventional reaction-diffusion models are limited in scope, as they fail to capture tumor dynamics under heterogeneous therapeutic paradigms. There is hence a critical need for computational frameworks that can realistically simulate SoC interventions while accounting for inter-patient variability in genomics, demographics, and treatment regimens. We introduce Standard-of-Care Digital Twin (SoC-DT), a differentiable framework that unifies reaction-diffusion tumor growth models, discrete SoC interventions (surgery, chemotherapy, radiotherapy) along with genomic and demographic personalization to predict post-treatment tumor structure on imaging. An implicit-explicit exponential time-differencing solver, IMEX-SoC, is also proposed, which ensures stability, positivity, and scalability in SoC treatment situations. Evaluated on both synthetic data and real world glioma data, SoC-DT consistently outperforms classical PDE baselines and purely data-driven neural models in predicting tumor dynamics. By bridging mechanistic interpretability with modern differentiable solvers, SoC-DT establishes a principled foundation for patient-specific digital twins in oncology, enabling biologically consistent tumor dynamics estimation. Code will be made available upon acceptance.

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