A genetically optimized physics-informed neural network for multiphysics modeling of magnetic hyperthermia in brain...

Magnetic hyperthermia therapy (MHT) in glioblastoma requires accurate modeling of nanoparticle transport and heat deposition across highly heterogeneous tumor regions. Traditional numerical approaches remain limited by high computational cost and sensitivity to complex tumor geometry, reducing their suitability for rapid clinical evaluation. To address these challenges, we introduce a genetically optimized physics-informed neural network (GA-PINN) that directly solves the bioheat transfer equation, while its governing parameters are dynamically coupled to nanoparticle transport, Darcy flow, and Arrhenius damage kinetics. Unlike prior PINN implementations, our approach integrates automatic genetic tuning of learning rates and loss-term weights, ensuring balanced convergence across coupled physics. Furthermore, tumor-focused collocation sampling uniquely enhances resolution of steep gradients near injection sites, a critical feature for patient-specific modeling. Results show that single-port injection restricts heating to the necrotic core, yielding central temperatures of 40 °C, 42.5 °C, and 48 °C for nanoparticle doses of 2.5, 5, and 10 kg/m³, respectively, but produces <10% necrosis in the viable rim. Increasing the magnetic field amplitude-frequency product to 8.4 × 10⁸ A/(m.s) raises peak temperatures to ∼47 °C and significantly accelerates damage accumulation. A multi-port injection strategy improves peripheral nanoparticle coverage, elevates rim temperatures to ∼41.5 °C, and reduces the dispersion index by more than 30%, indicating markedly more uniform ablation. These findings demonstrate that GA-PINN provides a stable, efficient, and physics-consistent surrogate for MHT, enabling rapid assessment and optimization of dosing conditions, magnetic field parameters, and multi-site injection strategies for patient-specific treatment planning.