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Akbarpour Ghazani M, Saghafian M, Jalali P, et al. Mathematical simulation and prediction of tumor volume using RBF artificial neural network at different circumstances in the tumor microenvironment. Proc Inst Mech Eng H. 2021;235(11):1335-1355doi: 10.1177/09544119211028380.
Akbarpour Ghazani, M., Saghafian, M., Jalali, P., & Soltani, M. (2021). Mathematical simulation and prediction of tumor volume using RBF artificial neural network at different circumstances in the tumor microenvironment. Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine, 235(11), 1335-1355. https://doi.org/10.1177/09544119211028380
Akbarpour Ghazani, Mehran, et al. "Mathematical simulation and prediction of tumor volume using RBF artificial neural network at different circumstances in the tumor microenvironment." Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine vol. 235,11 (2021): 1335-1355. doi: https://doi.org/10.1177/09544119211028380
Akbarpour Ghazani M, Saghafian M, Jalali P, Soltani M. Mathematical simulation and prediction of tumor volume using RBF artificial neural network at different circumstances in the tumor microenvironment. Proc Inst Mech Eng H. 2021 Nov;235(11):1335-1355. doi: 10.1177/09544119211028380. Epub 2021 Jul 10. PMID: 34247529; PMCID: PMC8573697.
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Proc Inst Mech Eng H. 2021 Nov;235(11):1335-1355. doi: 10.1177/09544119211028380. Epub 2021 Jul 10.

Mathematical simulation and prediction of tumor volume using RBF artificial neural network at different circumstances in the tumor microenvironment.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine

Mehran Akbarpour Ghazani, Mohsen Saghafian, Peyman Jalali, Madjid Soltani

Affiliations

  1. Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran.
  2. Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran.
  3. Faculty of Mechanical Engineering, University of Tabriz, Tabriz, Iran.
  4. Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada.
  5. Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, Canada.
  6. Advanced Bioengineering Initiative Center, Computational Medicine Center, K. N. Toosi University of Technology, Tehran, Iran.

PMID: 34247529 PMCID: PMC8573697 DOI: 10.1177/09544119211028380

Abstract

Uncontrolled proliferation of cells in a tissue caused by genetic mutations inside a cell is referred to as a tumor. A tumor which grows rapidly encounters a barrier when it grows to a certain size in presence of preexisting vasculature. This is the time when it has to find a way to go on the growth. The tumor starts to secrete tumor angiogenic factors (TAFs) and stimulate preexisting vessels to grow new sprouts. These new sprouts will find their way to the tumor in the extracellular matrix (ECM) by the gradient of TAF. As these new capillaries anastomose and reach tumor, fresh oxygen is available for the tumor and it will reinitiate the growth. Number of initial sprouts, distance of initial tumor cells from the vessel(s) and initial density of the tumor at the time of sprout formation are questions which are to be investigated. In the present study, the aim is to find the response of tumor cells and vessels to the reciprocal effects of each other in different circumstances in the tissue. Together with a mathematical formulation, a radial basis function (RBF) neural network is established to predict the number of tumor cells at different circumstances including size and distance of initial tumors from the parent vessel. A final formulation is given for the final number of tumor cells as a function of initial tumor size and distance between a parent vessel and a tumor. Results of this simulation demonstrate that, increasing the distance between a tumor and a parent vessel decreases the number of final tumor cells. Specially, this decrement becomes faster beyond a certain distance. Moreover, initial tumors in bigger domains must become much bigger before inducing angiogenesis which makes it harder for them to survive.

Keywords: Mathematical oncology; angiogenesis; artificial neural network; hybrid tumor modeling; radial basis function; vascular tumor

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