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Physics-Informed Neural Networks for Osteosarcoma Tumor-Immune Dynamics

Pasquale De Luca1,2,*, Livia Marcellino1

1 Department of Science and Technology, Parthenope University of Naples, Naples, Italy
2 Department for the Promotion of Human Sciences and Quality of Life, University of Rome San Raffaele, Rome, Italy

* Corresponding Author: Pasquale De Luca. Email: email

(This article belongs to the Special Issue: Advances in Mathematical Modeling: Numerical Approaches and Simulation for Computational Biology)

Computer Modeling in Engineering & Sciences 2026, 147(3), 27 https://doi.org/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 3.2×105. 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.

Keywords

Physics-informed neural networks; osteosarcoma; tumor-immune dynamics; poroelastic model; interferon-gamma; reaction-diffusion equations; computational oncology; mesh-free methods

Cite This Article

APA Style
De Luca, P., Marcellino, L. (2026). Physics-Informed Neural Networks for Osteosarcoma Tumor-Immune Dynamics. Computer Modeling in Engineering & Sciences, 147(3), 27. https://doi.org/10.32604/cmes.2026.082664
Vancouver Style
De Luca P, Marcellino L. Physics-Informed Neural Networks for Osteosarcoma Tumor-Immune Dynamics. Comput Model Eng Sci. 2026;147(3):27. https://doi.org/10.32604/cmes.2026.082664
IEEE Style
P. De Luca and L. Marcellino, “Physics-Informed Neural Networks for Osteosarcoma Tumor-Immune Dynamics,” Comput. Model. Eng. Sci., vol. 147, no. 3, pp. 27, 2026. https://doi.org/10.32604/cmes.2026.082664



cc Copyright © 2026 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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