Journal article
David A. Hormuth II, J. Weis, Stephanie L. Barnes, M. Miga, E. Rericha, V. Quaranta, T. Yankeelov
Physical Biology, 2015
Physical Biology, 2015
Semantic Scholar
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APA
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II, D. A. H., Weis, J., Barnes, S. L., Miga, M., Rericha, E., Quaranta, V., & Yankeelov, T. (2015). Predicting in vivo glioma growth with the reaction diffusion equation constrained by quantitative magnetic resonance imaging data. Physical Biology.
Chicago/Turabian
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II, David A. Hormuth, J. Weis, Stephanie L. Barnes, M. Miga, E. Rericha, V. Quaranta, and T. Yankeelov. “Predicting in Vivo Glioma Growth with the Reaction Diffusion Equation Constrained by Quantitative Magnetic Resonance Imaging Data.” Physical Biology (2015).
MLA
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II, David A. Hormuth, et al. “Predicting in Vivo Glioma Growth with the Reaction Diffusion Equation Constrained by Quantitative Magnetic Resonance Imaging Data.” Physical Biology, 2015.
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@article{david2015a,
title = {Predicting in vivo glioma growth with the reaction diffusion equation constrained by quantitative magnetic resonance imaging data},
year = {2015},
journal = {Physical Biology},
author = {II, David A. Hormuth and Weis, J. and Barnes, Stephanie L. and Miga, M. and Rericha, E. and Quaranta, V. and Yankeelov, T.}
}
Abstract
Reaction–diffusion models have been widely used to model glioma growth. However, it has not been shown how accurately this model can predict future tumor status using model parameters (i.e., tumor cell diffusion and proliferation) estimated from quantitative in vivo imaging data. To this end, we used in silico studies to develop the methods needed to accurately estimate tumor specific reaction–diffusion model parameters, and then tested the accuracy with which these parameters can predict future growth. The analogous study was then performed in a murine model of glioma growth. The parameter estimation approach was tested using an in silico tumor ‘grown’ for ten days as dictated by the reaction–diffusion equation. Parameters were estimated from early time points and used to predict subsequent growth. Prediction accuracy was assessed at global (total volume and Dice value) and local (concordance correlation coefficient, CCC) levels. Guided by the in silico study, rats (n = 9) with C6 gliomas, imaged with diffusion weighted magnetic resonance imaging, were used to evaluate the model’s accuracy for predicting in vivo tumor growth. The in silico study resulted in low global (tumor volume error <8.8%, Dice >0.92) and local (CCC values >0.80) level errors for predictions up to six days into the future. The in vivo study showed higher global (tumor volume error >11.7%, Dice <0.81) and higher local (CCC <0.33) level errors over the same time period. The in silico study shows that model parameters can be accurately estimated and used to accurately predict future tumor growth at both the global and local scale. However, the poor predictive accuracy in the experimental study suggests the reaction–diffusion equation is an incomplete description of in vivo C6 glioma biology and may require further modeling of intra-tumor interactions including segmentation of (for example) proliferative and necrotic regions.
