Brain Tumor Growth Simulation: Model Validation through Uncertainty Quantification

Brain Tumor Growth Simulation: Model Validation through Uncertainty Quantification

Brain tumors are one of the main causes of mortality and morbidity in the world and a critical issue in health risk. Tumor growth prediction can be a proper method for better understanding the phenomena and choosing the appropriate therapy for patients. Since tumors physiological and morphological properties vary vastly in different individuals, using patient specific models for modelling tumor growth is valuable in staging and personalized-therapy planning. There are different sources of uncertainties that affect model prediction accuracy and decision making for the therapy. In this paper, an image-based tumor growth model is evaluated by taking into account uncertainties in the model parameters. The proposed Reaction–Diffusion model integrates cancerous cell proliferation and invasion through reaction and diffusion terms, respectively. Uncertainties in diffusion and proliferation coefficients were analyzed through Monte Carlo method. The time needed for tumor to grow to its fatal size was estimated through numerical solution of the model. Comparison of the predicted time distribution with and without considering model parameter uncertainties shows a decrease in dispersity of predicted data that highlighted the importance of parameter uncertainty analysis in context of tumor growth modeling. Also, the wide range for survival time shows the importance of choosing proper parameters in order to enhance model accuracy.

Brain tumors are one of the main causes of mortality and morbidity in the world and a critical issue in health risk. Tumor growth prediction can be a proper method for better understanding the phenomena and choosing the appropriate therapy for patients. Since tumors physiological and morphological properties vary vastly in different individuals, using patient specific models for modelling tumor growth is valuable in staging and personalized-therapy planning. There are different sources of uncertainties that affect model prediction accuracy and decision making for the therapy. In this paper, an image-based tumor growth model is evaluated by taking into account uncertainties in the model parameters. The proposed Reaction–Diffusion model integrates cancerous cell proliferation and invasion through reaction and diffusion terms, respectively. Uncertainties in diffusion and proliferation coefficients were analyzed through Monte Carlo method. The time needed for tumor to grow to its fatal size was estimated through numerical solution of the model. Comparison of the predicted time distribution with and without considering model parameter uncertainties shows a decrease in dispersity of predicted data that highlighted the importance of parameter uncertainty analysis in context of tumor growth modeling. Also, the wide range for survival time shows the importance of choosing proper parameters in order to enhance model accuracy.