TY - EJOU
AU - Luca, Pasquale De
AU - Marcellino, Livia
TI - Physics-Informed Neural Networks for Osteosarcoma Tumor-Immune Dynamics
T2 - Computer Modeling in Engineering \& Sciences
PY -
VL -
IS -
SN - 1526-1506
AB - 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.
KW - Physics-informed neural networks; osteosarcoma; tumor-immune dynamics; poroelastic model; interferon-gamma; reaction-diffusion equations; computational oncology; mesh-free methods
DO - 10.32604/cmes.2026.082664