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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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Citations
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Book ChapterDOI

Emerging techniques in breast MRI

TL;DR: In this article , the authors focus on six emerging techniques that seek to quantitatively interrogate the physiological and biochemical properties of the breast, including dynamic contrast-enhanced MRI and magnetic resonance elastography.
Proceedings ArticleDOI

Indirect supervised fine-tuning of a tumor model parameter estimator neural network

TL;DR: In this paper , another supervised neural network was used to solve the applied differential equations faster with fewer algebraic steps than the traditionally used ODE solvers, which can be used in further research as an unconstrained optimization technique for parameter fitting.
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Memory effects on the proliferative function in the cycle-specific of chemotherapy

TL;DR: In this article, a generalized mathematical model of the breast and ovarian cancer is developed by considering the fractional differential equations with Caputo time-fractional derivatives, which shows that the time-evolution of the proliferating cell mass, the quiescent cell mass and the proliferative function are significantly influenced by their history.
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Modeling cancer’s ecological and evolutionary dynamics

TL;DR: In this article , a theoretical modeling framework, called the G -function, is presented to understand oncogenesis of cancer cells, which integrates both the ecology and evolution of cancer to understand cancerogenesis.
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Discrete ARMA Model Applied for Tumor Growth Inhibition Modeling and LQR-based Chemotherapy Optimization

TL;DR: An autoregressive moving average (ARMA) model for cancer tumor growth is estimated based on laboratory data of TGI in mice and was proven capable of describing with accuracy the tumor growth under single-agent chemotherapy.
References
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Journal ArticleDOI

Liquid biopsies come of age: towards implementation of circulating tumour DNA

TL;DR: The field is now in an exciting transitional period in which ctDNA analysis is beginning to be applied clinically, although there is still much to learn about the biology of cell-free DNA.
Journal ArticleDOI

Chemoresistance Evolution in Triple-Negative Breast Cancer Delineated by Single-Cell Sequencing

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Book ChapterDOI

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TL;DR: The death rate per tumor cell due to immunological response is proportional to the total number of antigen-producing (tumor) cells; thus, the total death rate is quadratic.
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