Model Comparison and Uncertainty Quantification in Tumor Growth
DOI:
https://doi.org/10.5540/tcam.2021.022.03.00495Palavras-chave:
Predictive oncology, Inverse problem, Allee effect, Logistic model, Gompertz model, Exponential modelResumo
Mathematical and computational modeling have been increasingly applied in many areas of cancer research, aiming to improve the understanding of tumorigenic mechanisms and to suggest more effective therapy protocols. The mathematical description of the tumor growth dynamics is often made using the exponential, logistic and Gompertz models. However, recent literature has suggested that the Allee effect may play an important role in the early stages of tumor dynamics, including cancer relapse and metastasis. This work investigates four distinct models with different complexities, which encompasses the exponential, logistic, Gompertz and weak and strong Allee effects dynamics. Using tumor growth data published in the literature, we focus on model selection following a wider approach. Specifically, we perform a sensitivity analysis, apply a Bayesian framework for parameter inference, evaluate the associated sensitivity matrices, and use different information criteria for model selection (Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), among others). We show that such a wider methodology allows having a more detailed picture of each model assumption and uncertainty, calibration reliability, ultimately improving tumor mathematical description. The used in vivo data revealed no evidence of the Allee effect in the growth dynamics.Referências
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