Digital Twin of Neuroblastoma Based on Morphometry and a Hybrid Differential Transform Method

A novel framework for constructing a personalized digital twin of neuroblastoma is presented, integrating morphometric analysis of histopathological H&E images, parameterization of a mechanistic tumor–immune–drug interaction model, and a spectral computational approach based on the multistep differential transform method. The proposed digital twin architecture consists of interconnected modules for image segmentation, quantitative morphometry, model parameter identification, tumor dynamics simulation, spectral prediction, and multi-regime therapy optimization. The core computational engine combines differential transform techniques, Adomian decomposition, and Padé approximation into a unified semi-analytical solver for nonlinear dynamical systems. This formulation enables the construction of continuous-time solutions without time-step discretization inherent to classical numerical integration schemes. As a result, patient-specific tumor dynamics can be efficiently simulated while preserving both numerical stability and biological interpretability. The model is calibrated using morphometric descriptors derived from routine histopathological data, allowing individualized parameterization of tumor growth and treatment response dynamics. Numerical experiments demonstrate that the proposed method achieves approximately a 200-fold reduction in computational cost compared to the classical fourth-order Runge-Kutta method (step size days, Python/NumPy implementation), while maintaining comparable predictive accuracy. The proposed approach provides a computationally efficient alternative to both classical numerical solvers and emerging AI-based surrogate models, while preserving direct mechanistic interpretability and enabling real-time simulation of personalized therapeutic scenarios in neuroblastoma.