Tracking glioblastoma progression after initial resection with minimal reaction-diffusion models

Duane C. Harris; Giancarlo Mignucci-Jiménez; Yuan Xu; Steffen E. Eikenberry; C. Chad Quarles; Mark C. Preul; Yang Kuang; Eric J. Kostelich; Duane C. Harris; Giancarlo Mignucci-Jiménez; Yuan Xu; Steffen E. Eikenberry; C. Chad Quarles; Mark C. Preul; Yang Kuang; Eric J. Kostelich

doi:10.3934/mbe.2022256

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 6: 5446-5481. doi: 10.3934/mbe.2022256

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Tracking glioblastoma progression after initial resection with minimal reaction-diffusion models

1.
School of Mathematical & Statistical Sciences, Arizona State University, Tempe, AZ 85281, USA
2.
Department of Neurosurgery, Barrow Neurological Institute, St. Joseph's Hospital and Medical Center, Phoenix, AZ 85013, USA

Received: 04 January 2022 Revised: 23 March 2022 Accepted: 23 March 2022 Published: 28 March 2022

We describe a preliminary effort to model the growth and progression of glioblastoma multiforme, an aggressive form of primary brain cancer, in patients undergoing treatment for recurrence of tumor following initial surgery and chemoradiation. Two reaction-diffusion models are used: the Fisher-Kolmogorov equation and a 2-population model, developed by the authors, that divides the tumor into actively proliferating and quiescent (or necrotic) cells. The models are simulated on 3-dimensional brain geometries derived from magnetic resonance imaging (MRI) scans provided by the Barrow Neurological Institute. The study consists of 17 clinical time intervals across 10 patients that have been followed in detail, each of whom shows significant progression of tumor over a period of 1 to 3 months on sequential follow up scans. A Taguchi sampling design is implemented to estimate the variability of the predicted tumors to using $ 144 $ different choices of model parameters. In $ 9 $ cases, model parameters can be identified such that the simulated tumor, using both models, contains at least 40 percent of the volume of the observed tumor. We discuss some potential improvements that can be made to the parameterizations of the models and their initialization.
- Fisher-Kolmogorov model,
- glioblastoma multiforme,
- ensemble prediction,
- parameter estimation,
- magnetic resonance imaging,
- reaction-diffusion equations
Citation: Duane C. Harris, Giancarlo Mignucci-Jiménez, Yuan Xu, Steffen E. Eikenberry, C. Chad Quarles, Mark C. Preul, Yang Kuang, Eric J. Kostelich. Tracking glioblastoma progression after initial resection with minimal reaction-diffusion models[J]. Mathematical Biosciences and Engineering, 2022, 19(6): 5446-5481. doi: 10.3934/mbe.2022256

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Abstract

We describe a preliminary effort to model the growth and progression of glioblastoma multiforme, an aggressive form of primary brain cancer, in patients undergoing treatment for recurrence of tumor following initial surgery and chemoradiation. Two reaction-diffusion models are used: the Fisher-Kolmogorov equation and a 2-population model, developed by the authors, that divides the tumor into actively proliferating and quiescent (or necrotic) cells. The models are simulated on 3-dimensional brain geometries derived from magnetic resonance imaging (MRI) scans provided by the Barrow Neurological Institute. The study consists of 17 clinical time intervals across 10 patients that have been followed in detail, each of whom shows significant progression of tumor over a period of 1 to 3 months on sequential follow up scans. A Taguchi sampling design is implemented to estimate the variability of the predicted tumors to using $ 144 $ different choices of model parameters. In $ 9 $ cases, model parameters can be identified such that the simulated tumor, using both models, contains at least 40 percent of the volume of the observed tumor. We discuss some potential improvements that can be made to the parameterizations of the models and their initialization.

References

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