TY  - JOUR
AU  - De Luca, Pasquale
AU  - Marcellino, Livia
PY  - 2026
TI  - Physics-Informed Neural Networks for Osteosarcoma Tumor-Immune Dynamics
JO  - Computer Modeling in Engineering & Sciences
SN  - 1526-1492
SP  - 27
VL  - 147
IS  - 3
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.
UR  - https://doi.org/10.32604/cmes.2026.082664
DO  - 10.32604/cmes.2026.082664