Radiotherapy cancer treatment model with fractional derivative coupled with linear-quadratic model

A mathematical model that simulates a radiotherapy cancer treatment process is presented in this thesis. The model takes two important radiobiological factors into consideration, which are repair and repopulation of cells. The model is used to simulate the fractionated radiotherapy treatment processes of six patients. The results give the population changes in the cells and the final volumes occupied by the normal and cancer cells. The model is formulated by integrating the Caputo fractional derivative with the previous cancer treatment model. Thereafter, the linear quadratic with the repopulation model is coupled into the model to account for the cells’ population decay due to radiation. The treatment processes are then simulated in MATLAB with numerical variables, numerical parameters, and radiation parameters. The numerical parameters which include the proliferation coefficients of cells, competition coefficients of cells, and the perturbation constant of the normal cells are obtained from a previous research. The radiation parameters are obtained from another previous research that reported clinical data of six patients treated with radiotherapy. From the reported clinical data, the patients had tumor volumes of 24.1cm 3, 17.4cm 3, 28.4cm 3 , 18.8cm 3, 3°.6cm3, and 12.6cm 3 and were treated with fractionated doses of 2.0 Gy for the first two patients and 1.8 Gy for the other four. Next, the integrity of the formulated model is established with the proof of the existence of unique solutions, the stability analysis, the sensitivity analysis, the bifurcation analysis, and the comparative analysis. Also, 96 radiation protocols are simulated by using the biologically effective dose formula. All these protocols are then used to obtain regression equations connecting the value of the Caputo fractional derivative with the fractionated radiation dose, and these regression equations are used to simulate various radiotherapy treatments in four different categories. The final tumor volumes, from the results of the simulations, are 3.58cm3 , 8.61cm3 , 5.68cm3 , 4.36cm3 , 5.75cm3 , and 6.12cm3. Meanwhile the volumes occupied by the normal cells are 23.87cm3, 17.29cm3, 28.1lcm3, 18.68cm3, 30.33cm3 , and 12.55cm3. The stability analysis shows that the model is asymptotically and exponentially stable. Also, the solutions of the simulations are unique and stable even there are changes in initial values. The sensitivity analysis shows that the most sensitive controllable model factor is the value of the Caputo fractional derivative and this model factor has bifurcation values. Furthermore, the comparative analysis shows that the fractional derivative model encompasses the memory effect of the radiotherapy process. The predicted simulated final tumor volumes obtained with the regression equations are then compared with the corresponding reported clinical final tumor volumes. The results of these comparisons show that the predictions have minimal errors, hence they are acceptable. Finally, optimal and complete treatment solutions are simulated and predicted

Analysis and analytical simulation for a biophysical fractional diffusive cancer model with virotherapy using the Caputo operator

In this paper, a biophysical fractional diffusive cancer model with virotherapy is thoroughly analyzed and analytically simulated. The goal of this biophysical model is to represent both the dynamics of cancer development and the results of virotherapy, which uses viruses to target and destroy cancer cells. The Caputo sense is applied to the fractional derivatives. We look at the governing model's existence and uniqueness. For analytical solutions, the Laplace residual power series approach is used. The study investigates the model's dynamic behavior, shedding light on the development of cancer and the effects of virotherapy. The research advances our knowledge of cancer modeling and treatment options. Numerical simulations show the agreement between the analytical results and the related numerical solutions, proving the usefulness of the analytical solution

Nonlinear analysis of a fractional reaction diffusion model for tumour invasion

Mathematical models in general an

Mathematical models for glioma growh and migration inside the brain

284 p.Los gliomas forman el subtipo más prevalente, agresivo e invasivo de tumores cerebrales primarios,caracterizados por una rápida proliferación celular y una elevada capacidad de infiltración. A pesar de los avances de la investigación clínica, estos tumores suelen ser resistentes al tratamiento; la supervivencia media oscila entre 9 y 12 meses, siendo la recurrencia la principal causa de mortalidad.La migración y la invasión de los gliomas en el cerebro son fenómenos complejos y aún se desconocen varios de los mecanismos subyacentes que guían la progresión de estos tumores.En esta tesis, proponemos varios modelos matemáticos para estudiar diversos aspectos de la progresión del glioma en relación con las escalas microscópicas y macroscópicas que caracterizan este proceso. Considerar el carácter intrínsico multiescala de la evolución del glioma permite definir modelos basados en sistemas dinámicos, ecuaciones cinéticas y EDP macroscópicas con diferentes roles dependiendo de los fenómenos a estudiar. Uno de los objetivos principales de esta tesis es integrar datos biológicos y clínicos con los modelos matemáticos. Los datos experimentales utilizados se han obtenido de imágenes por resonancia magnética, de imágenes con tensor de difusión del cerebro humano y de análisis de inmunofluorescencia in vivo de distribuciones de varias proteínas en Drosophila, un modelo fiable para el estudio de la dinámica del glioblastoma.Analizamos las características de anisotropía del tejido nervioso, utilizando los datos del tensor de difusión, y la influencia de la estructura de las fibras en la dinámica de las células tumorales.Mostramos cómo la red de fibras guía la migración celular a lo largo de rutas preferenciales,reproduciendo los patrones ramificados y heterogéneos típicos de la evolución del glioma; asimismo,demostramos cómo los tratamientos multimodales pueden reducir este comportamiento.Estudiamos la interdependencia entre la acidez del microambiente y la vascularización en el proceso de angiogénesis tumoral. Para ello, construimos un modelo capaz de reproducir la influencia de estos mecanismos en el desarrollo de la heterogeneidad intratumoral y de características típicas de la progresión del glioma relacionadas con la hipoxia (e.g. la necrosis). Este estudio permite formular una clasificación de los tumores basada en el nivel de necrosis, así como la investigación de terapias multimodales que incluyan efectos antiangiogénicos.Investigamos la influencia de las protrusiones celulares desde una perspectiva no local.Analizamos su rol en el fenómeno de la guía por contacto y en la manifestación de efectos colaborativos o competitivos entre dos estímulos que determinan cambios de dirección de la velocidad celular.Utilizando el análisis experimental de las distribuciones de varias proteínas, evaluamos la relación de las protrusiones celulares con las integrinas y las proteasas como principales mecanismos de progresión del glioblastoma. Mostramos cómo las interacciones bioquímicas y biomecánicas de estos agentes dan como resultado el desarrollo de frentes de propagación tumoral, que pueden presentar una evolución dinámica y heterogénea en relación a los cambios ambientales.bcam:basque center for applied mathematics; La Caixa Foundatio

Mathematical biomedicine and modeling avascular tumor growth

In this chapter we review existing continuum models of avascular tumor growth, explaining howthey are inter related and the biophysical insight that they provide. The models range in complexity and include one-dimensional studies of radiallysymmetric growth, and two-dimensional models of tumor invasion in which the tumor is assumed to comprise a single population of cells. We also present more detailed, multiphase models that allow for tumor heterogeneity. The chapter concludes with a summary of the different continuum approaches and a discussion of the theoretical challenges that lie ahead

International Conference on Mathematical Analysis and Applications in Science and Engineering – Book of Extended Abstracts

The present volume on Mathematical Analysis and Applications in Science and Engineering - Book of Extended Abstracts of the ICMASC’2022 collects the extended abstracts of the talks presented at the International Conference on Mathematical Analysis and Applications in Science and Engineering – ICMA2SC'22 that took place at the beautiful city of Porto, Portugal, in June 27th-June 29th 2022 (3 days). Its aim was to bring together researchers in every discipline of applied mathematics, science, engineering, industry, and technology, to discuss the development of new mathematical models, theories, and applications that contribute to the advancement of scientific knowledge and practice. Authors proposed research in topics including partial and ordinary differential equations, integer and fractional order equations, linear algebra, numerical analysis, operations research, discrete mathematics, optimization, control, probability, computational mathematics, amongst others. The conference was designed to maximize the involvement of all participants and will present the state-of- the-art research and the latest achievements.info:eu-repo/semantics/publishedVersio

Fractional model of cancer immunotherapy and its optimal control

Cancer is one of the most serious illnesses in all of the world. Although most of the cancer patients are treated with chemotherapy, radiotherapy and surgery, wide research is conducted related to experimental and theoretical immunology. In recent years, the research on cancer immunotherapy has led to major medical advances. Cancer immunotherapy refers to the stimulation of immune system to deal with cancer cells. In medical practice, it is mainly achieved by using effector cells such as activated T-cells and Interleukin-2 (IL-2), which is the main cytokine responsible for lymphocyte activation, growth and differentiation. A well-known mathematical model, named as Kirschner-Panetta (KP) model, represents richly the dynamics of the interaction between cancer cells, IL-2 and the effector cells. The dynamics of the KP model is described and the solution to which is approximated by using polynomial approximation based methods such as Adomian decomposition method and differential transform method. The rich nonlinearity of the KP model causes these approaches to become so complicated in order to deal with the representation of polynomial approximations. It is illustrated that the approximated polynomials are in good agreement with the solution obtained by common numerical approaches. In the KP model, the growth of the tumour cells can be expressed by a linear function or any limited-growth function such as logistic equation, in which the cancer population possesses an upper bound mentioned as carrying capacity. Effector cells and IL-2 construct two external sources of medical treatment to stimulate immune system to eradicate cancer cells. Since the main goal in immunotherapy is to remove the tumour cells with the least probable medication side effects, an advanced version of the model may include a time dependent external sources of medical treatment, meaning that the external sources of medical treatment could be considered as control functions of time and therefore the optimum use of medical sources can be evaluated in order to achieve the optimal measure of an objective function. With this sense of direction, two distinct strategies are explored. The first one is to only consider the external source of effector cells as the control function to formulate an optimal control problem. It is shown under which circumstances, the tumour is eliminated. The approach in the formulation of the optimal control is the Pontryagin maximum principal. Furthermore the optimal control problem will be dealt with using particle swarm optimization (PSO). It is shown that the obtained results are significantly better than those obtained by previous researchers. The second strategy is to formulate an optimal control problem by considering both the two external sources as the controls. To our knowledge, it is the first time to present a multiple therapeutic protocol for the KP model. Some MATLAB routines are develop to solve the optimal control problems based on Pontryagin maximum principal and also the PSO. As known, fractional differential equations are more appropriate to describe the persistent memory of physical phenomena. Thus, the fractional KP model is defined in the sense of Caputo differentiation operator. An effective method for numerical treatment of the model is described, namely Predictor-Corrector method of Adams-Bashforth-Moulton type. A robust MATLAB routine is coded based on the mentioned approach and the solution obtained will be compared with those of the classical KP model. The code is prepared in such a way to be able to deal with systems of fractional differential equations, in which each equation has its own fractional order (i.e. multi-order systems of fractional differential equations). The theorems for existence of solutions and the stability analysis of the fractional KP model are represented. In this regard, a frequently used method of solving fractional differential equations (FDEs) is described in details, namely multi-step generalized differential transform method (MSGDTM), then it is illustrated that the method neglects the persistent memory property and takes the incorrect approach in dealing with numerical solutions of FDEs and therefore it is unfit to be used in differential equations governed by fractional differentiation operators. The sigmoidal behavior of the solution to the logistic equation caused it to be one of the most versatile models in natural sciences and therefore the fractional logistic equation would be a relevant problem to be dealt with. Thus, a power series of Mittag-Leffer functions is introduced, the behaviour of which is in good agreement with the solution to fractional logistic equation (FLE), and then a fractional integro-differential equation is represented and proved to be satisfied with the power series of Mittag-Leffler function. The obtained fractional integro-differential equation is named as modified fractional differential equation (MFDL) and possesses a nonlinear additive term related to the solution of the logistic equation (LE). The method utilized in the thesis, may be appropriately applied to the analysis of solutions to nonlinear fractional differential equations of mathematical physics. Inverse problems to FDEs occur in many branches of science. Such problems have been investigated, for instance, in fractional diffusion equation and inverse boundary value problem for semi- linear fractional telegraph equation. The determination of the order of fractional differential equations is an issue, which has been analyzed and discussed in, for instance, fractional diffusion equations. Thus, fractional order estimation has been conducted for some classes of linear fractional differential equations, by introducing the relationship between the fractional order and the asymptotic behaviour of the solutions to linear fractional differential equations. Fractional optimal control problems, in which the system and (or) the objective function are described based on fractional derivatives, are much more complicated to be solved by using a robust and reliable numerical approach. Thus, a MATLAB routine is provided to solve the optimal control for fractional KP model and the obtained solutions are compared with those of classical KP model. It is shown that the results for fractional optimal control problems are better than classical optimal control problem in the sense of the amount of drug administration

Um segundo estudo de invasão populacional dinâmica a partir da equação do telégrafo reativo e formulação de elementos de contorno - Um ensaio sobre o crescimento tumoral in vitro

This paper is a continuation of a study already carried out on the use of the reactive-telegraph equation to analyse problems of population dynamics based on a formulation of the boundary element method (BEM). In this paper, the numerical model simulates the evolution of a tumour as a problem of population density of cancer cells from different reactive terms coupled to the reactive-telegraph equation to describe the growth and distribution of the population, similar to the two-dimensional in vitro tumour growth experiment. The mathematical model developed is called D-BEM, uses a time independent fundamental solution and the finite difference method is combined with BEM to approximate the time derivative terms and the Gaussian quadrature is used to calculate the domain integrals. The solution of the system nonlinear of equations is based on the Gaussian elimination method and the stability of the proposed formulation was verified. As the telegraph equation has a wave behaviour, a phase change phenomenon that can lead to the appearance of negative population density may occur, an algorithm was developed to guarantee the solution's positivity. Important results were obtained and demonstrate the effect of the delay parameter on tumour growth. In one of the tested cases, the results indicated an oscillatory behaviour in the tumour growth when the delay parameter assumed increasing values. The results of numerical simulations that sought to represent tumour growth, as well as the entire formulation of the boundary elements are presented below.Este artigo é a continuação de um estudo já realizado sobre o uso da equação do telégrafo reativo para analisar problemas de dinâmica populacional a partir de uma formulação do método dos elementos de contorno (BEM). Neste artigo, o modelo numérico simula a evolução de um tumor como um problema de densidade populacional de células cancerosas a partir de diferentes termos reativos acoplados à equação do telégrafo reativo para descrever o crescimento e distribuição da população, semelhante ao experimento de crescimento do tumor in vitro. O modelo matemático desenvolvido é denominado D-BEM, usa uma solução fundamental independente do tempo e o método das diferenças finitas é combinado com o BEM para aproximar os termos de tempo derivativos e a quadratura Gaussiana é usada para calcular as integrais de domínio. A solução do sistema de equações é baseada no método de eliminação gaussiana e foi verificada a estabilidade da formulação proposta. Como a equação do telégrafo possui comportamento ondulatório, pode ocorrer um fenômeno de mudança de fase que pode levar ao aparecimento de densidade populacional negativa, para tanto, foi desenvolvido um algoritmo que garantir a positividade da solução. Resultados importantes foram obtidos e demonstram o efeito do parâmetro de atraso no crescimento do tumor. Em um dos casos testados, os resultados indicaram um comportamento oscilatório no crescimento tumoral quando o parâmetro de retardo assumiu valores crescentes. O importante resultado dessa antítese para o crescimento do tumor, bem como toda a formulação dos elementos de contorno são apresentados a seguir