Optimization of Multi-Stage Neuroblastoma Therapy Based on Differential Transformations

Оптимизация многоэтапной терапии нейробластомы на основе дифференциальных преобразований
Andriy Gusynin
2026-06-14

differential transform methodmulti-stage neuroblastoma therapyoptimal controlpharmacokinetic modelingterminal control
A numerical-analytical method for optimizing multi-stage high-risk neuroblastoma therapy is proposed, based on the differential transform method and a terminal control framework. The mathematical model of the “tumor-neuropil-immunity-pharmacokinetics” system is formulated in spectral form, enabling optimal control synthesis without numerical integration of the system of differential equations. The proposed approach yields a compact recurrent representation of the dynamics, an analytical form of the optimal control law, and a closed-loop control algorithm robust to parameter perturbations and individual patient responses. It is shown that the method provides significant computational acceleration compared to gradient-based optimization, making it suitable for near-real-time clinical decision support systems.
1
A closed-loop control algorithm is designed to remain robust under parameter perturbations and individual patient responses.
2
A numerical-analytical method optimizes multi-stage high-risk neuroblastoma therapy using differential transforms and terminal control.
3
The approach produces a compact recurrent dynamics representation and an analytical optimal control law.
4
The method substantially accelerates computation compared with gradient-based optimization, supporting near-real-time clinical decision support.
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The tumor-neuropil-immunity-pharmacokinetics model is represented spectrally, enabling optimal-control synthesis without numerically integrating differential equations.

the tumor–neuropil–immunity–pharmacokinetics system in multi-stage high-risk neuroblastoma therapy

optimal multi-stage therapy control, including the analytical control law, robustness to parameter and individual-response perturbations, and computational efficiency

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2026-06-14
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Andriy Gusynin
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