Abstract

We present comprehensive mathematical modeling of radiopharmaceutical spatiotemporal distributions within vascularized solid tumors. The novelty of the presented model is at mathematical level. From the mathematical viewpoint, we provide a general modeling framework for the process of radiopharmaceutical distribution in the tumor microenvironment to enable an analysis of the effect of various tumor-related parameters on the distribution of different radiopharmaceuticals. We argue that partial differential equations (PDEs), beyond conventional methods, including ODE-based kinetic compartment modeling, can be used to evaluate radiopharmaceutical distribution in both time and space. In addition, we consider the spatially-variable dynamic structure of tumor microvascular networks to simulate blood flow distribution. To examine the robustness of the model, the effects of microvessel density (MVD) and tumor size, as two important factors in tumor prognosis, on the radiopharmaceutical distribution within the tumor are investigated over time (in the present work, we focus on the radiopharmaceutical [18F]FDG, yet the framework is broadly applicable to radiopharmaceuticals). Results demonstrate that the maximum total uptake of [18F]FDG at all time frames occurs in the tumor area due to the high capillary permeability and lack of a functional lymphatic system. As the MVD of networks increases, the mean total uptake in the tumor is also enhanced, where the rate of diffusion from vessel to tissue has the highest contribution and the rate of convection transport has the lowest contribution. The results of this study can be used to better investigate various phenomena and bridge a gap among cancer biology, mathematical oncology, medical physics, and radiology.

Details

Title
Spatiotemporal multi-scale modeling of radiopharmaceutical distributions in vascularized solid tumors
Author
Kiani Shahvandi, Mohammad 1 ; Soltani, M. 2 ; Moradi Kashkooli, Farshad 1 ; Saboury, Babak 3 ; Rahmim, Arman 4 

 K. N. Toosi University of Technology, Department of Mechanical Engineering, Tehran, Iran (GRID:grid.411976.c) (ISNI:0000 0004 0369 2065) 
 K. N. Toosi University of Technology, Department of Mechanical Engineering, Tehran, Iran (GRID:grid.411976.c) (ISNI:0000 0004 0369 2065); University of Waterloo, Department of Electrical and Computer Engineering, Waterloo, Canada (GRID:grid.46078.3d) (ISNI:0000 0000 8644 1405); University of Waterloo, Centre for Biotechnology and Bioengineering (CBB), Waterloo, Canada (GRID:grid.46078.3d) (ISNI:0000 0000 8644 1405); K. N. Toosi University of Technology, Advanced Bioengineering Initiative Center, Multidisciplinary International Complex, Tehran, Iran (GRID:grid.411976.c) (ISNI:0000 0004 0369 2065) 
 Hospital of the University of Pennsylvania, Department of Radiology, Philadelphia, USA (GRID:grid.411115.1) (ISNI:0000 0004 0435 0884); BC Cancer Research Institute, Department of Integrative Oncology, Vancouver, Canada (GRID:grid.411115.1) 
 BC Cancer Research Institute, Department of Integrative Oncology, Vancouver, Canada (GRID:grid.411115.1); University of British Columbia, Departments of Radiology and Physics, Vancouver, Canada (GRID:grid.17091.3e) (ISNI:0000 0001 2288 9830) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2707112936
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.