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Open AccessJournal ArticleDOI

A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors

TLDR
The opportunities of a model‐based approach as discussed in this review can be of great benefit for future optimizing and personalizing anticancer treatment.
Abstract
Increasing knowledge of intertumor heterogeneity, intratumor heterogeneity, and cancer evolution has improved the understanding of anticancer treatment resistance. A better characterization of cancer evolution and subsequent use of this knowledge for personalized treatment would increase the chance to overcome cancer treatment resistance. Model-based approaches may help achieve this goal. In this review, we comprehensively summarized mathematical models of tumor dynamics for solid tumors and of drug resistance evolution. Models displayed by ordinary differential equations, algebraic equations, and partial differential equations for characterizing tumor burden dynamics are introduced and discussed. As for tumor resistance evolution, stochastic and deterministic models are introduced and discussed. The results may facilitate a novel model-based analysis on anticancer treatment response and the occurrence of resistance, which incorporates both tumor dynamics and resistance evolution. The opportunities of a model-based approach as discussed in this review can be of great benefit for future optimizing and personalizing anticancer treatment.

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References
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Computational oncology — mathematical modelling of drug regimens for precision medicine

TL;DR: The current achievements and limitations with regard to computational modelling of drug regimens are highlighted, and the potential future implementation of this strategy to achieve precision medicine in oncology is discussed.
Journal ArticleDOI

Quantitative Metrics of Net Proliferation and Invasion Link Biological Aggressiveness Assessed by MRI with Hypoxia Assessed by FMISO-PET in Newly Diagnosed Glioblastomas

TL;DR: This model has already been proven useful as a novel tool to dynamically quantify the net rates of proliferation (rho) and invasion (D) of the glioma cells in individual patients and can be calculated from routinely available pretreatment MRI in vivo.
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Clinically relevant modeling of tumor growth and treatment response.

TL;DR: In this article, the authors proposed the use of emerging, quantitative tumor imaging methods to initialize a new generation of predictive models, which could be able to forecast clinical outputs, such as overall response to treatment and time to progression, which will provide opportunities for guided intervention and improved patient care.
Journal ArticleDOI

A Review of Mixed-Effects Models of Tumor Growth and Effects of Anticancer Drug Treatment Used in Population Analysis

TL;DR: This selection of models with published mathematical equations aimed at describing the dynamics of tumor size in cancer patients following anticancer drug treatment will constitute the basis for the Drug Disease Model Resources (DDMoRe) repository for models on oncology.
Journal ArticleDOI

Exploiting Evolution To Treat Drug Resistance: Combination Therapy and the Double Bind

TL;DR: A general evolutionary game theory framework of a double bind is presented to study the effect that such an approach would have in cancer and an explanation for its effectiveness based on the commensalistic relationship between the tumor phenotypes is provided.
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