Predicting in vivo XXXX growth with the reaction diffusion equation constrained by quantitative magnetic resonance imaging data.

@entity39 @entity19 @entity5 @entity36 @entity11 @entity35
Reaction-diffusion models have been widely used to model @entity11 growth. However, it has not been shown how accurately this model can predict @entity5 status using model parameters (i.e., @entity5 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 @entity5 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 @entity19 model of @entity11 growth. The parameter estimation approach was tested using an in silico @entity5 '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, @entity35 (n  =  9) with @entity36 , imaged with diffusion weighted magnetic resonance imaging, were used to evaluate the model's accuracy for predicting in vivo @entity5 growth. The in silico study resulted in low global ( @entity5 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 ( @entity5 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 @entity5 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 @entity11 biology and may require further modeling of intra- @entity5 interactions including segmentation of (for example) proliferative and @entity39 regions.
