A Mechano-Chemical Model of a Solid Tumor for Therapy Outcome Predictions

Abstract

Abstract. Experimental investigations of tumors often result in data reflecting very complex underlying mechanisms. Computer models of such phenomena enable their analysis and may lead to novel and more efficient therapy strategies. We present a generalized finite element mechanochemical model of a solid tumor and assess its suitability for predicting therapy outcome. The model includes hosting tissue, tumor cells (vital and necrotic), nutrient, blood vessels and a growth inhibitor. At a certain time instant of the tumor development virtual therapies are performed and their outcomes are presented. The model is initiated with parameters either obtained directly from the available literature or estimated using multi-scale modeling. First results indicate the usefulness of multiphysics tumor models for predicting therapy response.

A novel medical image data-based multi-physics simulation platform for computational life sciences

“…When modelling hyperthermia treatment of cancer, the tumour model was extended by considering the temperature dependence of the perfusion (and related nutrient delivery), the direct temperature-induced apoptosis as well as indirect cell killing owing to the enhanced activity of a chemotherapeutic agent rsfs.royalsocietypublishing.org Interface Focus 3: 20120058 such as doxorubicin [13]. The model was also used to assess whether the local administration of angiogenesis inhibitors can lead to effective tumour treatment or only causes the vasculature to circumvent the treated area [14]. In a next step, it will be important to validate the model beyond the phenomenological, macroscopic level and to continue including other relevant aspects such as interstitial fluid pressure to increase the realism of the model.…”

Comparison of Cloud and Local HPC Approach for MUSCLE-based Multiscale Simulations

“…Examples of such simulations include e.g. blood flow simulations (assisting in the treatment of in-stent restenosis) [8], solid tumor models [9], stellar system simulations [4] or virtual reactor [10]. The requirements of such applications are addressed by numerous partial solutions.…”

A Generative Approach for Image-Based Modeling of Tumor Growth

“…Tumor models in medical image analysis rely almost exclusively on information from morphologic images and consequently focus on the macroscopic phenomena of tumor evolution. An important class of macroscopic tumor models is based on the reaction-diffusion equations [5,24,6,1,14,23,13,16,10]. In this paper, we propose an efficient Bayesian framework for image-based inference in this type of tumor growth models.…”