@article{Bektemessov2024, 
author = {Zholaman Bektemessov and Laurence Cherfils and Cyrille Allery and Julien Berger and Elisa Serafini and Eleonora Dondossola and Stefano Casarin},
title = {On a data-driven mathematical model for prostate cancer bone metastasis},
year = {2024},
journal = {AIMS Mathematics},
volume = {9},
number = {12},
pages = {34785-34805},
keywords = {prostate cancer, bone metastasis, tumor growth, PDE model, simulation, in vivo-in silico modeling, parameter estimation, inverse problems},
url = {https://www.sciopen.com/article/10.3934/math.20241656},
doi = {10.3934/math.20241656},
abstract = {Prostate cancer bone metastasis poses significant health challenges, affecting countless individuals. While treatment with the radioactive isotope radium-223 ( 223Ra) has shown promising results, there remains room for therapy optimization. In vivo studies are crucial for optimizing radium therapy; however, they face several roadblocks that limit their effectiveness. By integrating in vivo studies with in silico models, these obstacles can be potentially overcome. Existing computational models of tumor response to  223Ra are often computationally intensive. Accordingly, we here present a versatile and computationally efficient alternative solution. We developed a PDE mathematical model to simulate the effects of  223Ra on prostate cancer bone metastasis, analyzing mitosis and apoptosis rates based on experimental data from both control and treated groups. To build a robust and validated model, our research explored three therapeutic scenarios: no treatment, constant  223Ra exposure, and decay-accounting therapy, with tumor growth simulations for each case. Our findings align well with experimental evidence, demonstrating that our model effectively captures the therapeutic potential of  223Ra, yielding promising results that support our model as a powerful infrastructure to optimize bone metastasis treatment.}
}