@article{De Luca2026, 
author = {Pasquale De Luca and Livia Marcellino},
title = {Physics-Informed Neural Networks for Osteosarcoma Tumor-Immune Dynamics},
year = {2026},
journal = {Computer Modeling in Engineering & Sciences},
volume = {147},
number = {3},
pages = {27},
keywords = {Physics-informed neural networks, osteosarcoma, tumor-immune dynamics, poroelastic model, interferon-gamma, reaction-diffusion equations, computational oncology, mesh-free methods},
url = {https://www.sciopen.com/article/10.32604/cmes.2026.082664},
doi = {10.32604/cmes.2026.082664},
abstract = {Osteosarcoma is the most common primary malignant bone tumor in pediatric populations. This work presents an extended Physics-Informed Neural Network framework that incorporates interferon-gamma (IFN- γ) as a fifth biological variable, complementing previous four-variable formulations with an explicit cytokine-mediated macrophage activation pathway. The model couples five biological fields with mechanical tissue response through Biot’s poroelastic theory over a two-dimensional domain. Four distinct initial macrophage distributions were investigated. Numerical stability was achieved across all scenarios, with total loss values between 0.056 and 0.158 and mechanical residuals below  3.2×10−5. The boundary-concentrated configuration yielded the lowest biological loss. Predicted dynamics are biologically consistent, exhibiting initial immune-mediated suppression followed by progressive macrophage depletion. Comparison of the four scenarios suggests that spatial co-localization between macrophages and tumor boundaries enhances early immune-tumor contact via pressure-driven advection, while sustained immune engagement leads to measurable macrophage exhaustion. Temporal stiffness introduced by the rapid interferon-gamma decay was managed through curriculum learning and adaptive loss weighting.}
}