Paper Info
Title
Model Predictive Control For Optimal Treatment In A Spatial Cancer Game
Abstract
This work focuses on modeling tumorigenesis as a spatial evolutionary game and on finding optimal cancer treatment using a model predictive control approach. Extending a nonspatial cancer game from the literature into a spatial setting, we consider a solid tumor composed of cells of two different types: proliferative and motile. In our agent-based spatial game, cells represent vertices of an undirected dynamic graph where a link between any two cells indicates that these cells can interact with each other. A focal cell can reproduce only if it interacts with another cell, where the proliferation probabilities are given by the fitness matrix of the original nonspatial game. Without treatment, the cancer cells grow exponentially. Subsequently, we use nonlinear model predictive control to find an optimal time-varying treatment, with an objective representing a trade-off between minimization of the tumor mass and treatment toxicity. As for example androgen-deprivation treatment in metastatic castrate-resistant prostate cancer, this treatment is assumed to decrease the chances for interaction between the cancer cells and hereby decrease cells' proliferation rate. In case studies, we show that the optimal treatment often leads to a decrease of the tumor mass. This suggests that model predictive control has a high potential in designing cancer treatments.
Year
Venue
Keywords
2017
2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC)
cancer modeling and treatment, spatial evolutionary game theory, nonlinear model predictive control, dynamic graphs
Field
DocType
ISSN
Carcinogenesis,Graph,Mathematical optimization,Cancer cell,Computer science,Model predictive control,Prostate cancer,Nonlinear model,Cancer,Exponential growth
Conference
0743-1546
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Muros, F.J.1113.70
Jose Maria Maestre23214.98
Li You310.96
Katerina Stankova400.34