Literature DB >> 25563250

A voxel-based multiscale model to simulate the radiation response of hypoxic tumors.

I Espinoza1, P Peschke2, C P Karger3.   

Abstract

PURPOSE: In radiotherapy, it is important to predict the response of tumors to irradiation prior to the treatment. This is especially important for hypoxic tumors, which are known to be highly radioresistant. Mathematical modeling based on the dose distribution, biological parameters, and medical images may help to improve this prediction and to optimize the treatment plan.
METHODS: A voxel-based multiscale tumor response model for simulating the radiation response of hypoxic tumors was developed. It considers viable and dead tumor cells, capillary and normal cells, as well as the most relevant biological processes such as (i) proliferation of tumor cells, (ii) hypoxia-induced angiogenesis, (iii) spatial exchange of cells leading to tumor growth, (iv) oxygen-dependent cell survival after irradiation, (v) resorption of dead cells, and (vi) spatial exchange of cells leading to tumor shrinkage. Oxygenation is described on a microscopic scale using a previously published tumor oxygenation model, which calculates the oxygen distribution for each voxel using the vascular fraction as the most important input parameter. To demonstrate the capabilities of the model, the dependence of the oxygen distribution on tumor growth and radiation-induced shrinkage is investigated. In addition, the impact of three different reoxygenation processes is compared and tumor control probability (TCP) curves for a squamous cells carcinoma of the head and neck (HNSSC) are simulated under normoxic and hypoxic conditions.
RESULTS: The model describes the spatiotemporal behavior of the tumor on three different scales: (i) on the macroscopic scale, it describes tumor growth and shrinkage during radiation treatment, (ii) on a mesoscopic scale, it provides the cell density and vascular fraction for each voxel, and (iii) on the microscopic scale, the oxygen distribution may be obtained in terms of oxygen histograms. With increasing tumor size, the simulated tumors develop a hypoxic core. Within the model, tumor shrinkage was found to be significantly more important for reoxygenation than angiogenesis or decreased oxygen consumption due to an increased fraction of dead cells. In the studied HNSSC-case, the TCD50 values (dose at 50% TCP) decreased from 71.0 Gy under hypoxic to 53.6 Gy under the oxic condition.
CONCLUSIONS: The results obtained with the developed multiscale model are in accordance with expectations based on radiobiological principles and clinical experience. As the model is voxel-based, radiological imaging methods may help to provide the required 3D-characterization of the tumor prior to irradiation. For clinical application, the model has to be further validated with experimental and clinical data. If this is achieved, the model may be used to optimize fractionation schedules and dose distributions for the treatment of hypoxic tumors.

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Year:  2015        PMID: 25563250     DOI: 10.1118/1.4903298

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  4 in total

1.  A radiobiological model of radiotherapy response and its correlation with prognostic imaging variables.

Authors:  Mireia Crispin-Ortuzar; Jeho Jeong; Andrew N Fontanella; Joseph O Deasy
Journal:  Phys Med Biol       Date:  2017-01-31       Impact factor: 3.609

Review 2.  Multiscale modeling methods in biomechanics.

Authors:  Pinaki Bhattacharya; Marco Viceconti
Journal:  Wiley Interdiscip Rev Syst Biol Med       Date:  2017-01-19

3.  Tumor radio-sensitivity assessment by means of volume data and magnetic resonance indices measured on prostate tumor bearing rats.

Authors:  Antonella Belfatto; Derek A White; Ralph P Mason; Zhang Zhang; Strahinja Stojadinovic; Guido Baroni; Pietro Cerveri
Journal:  Med Phys       Date:  2016-03       Impact factor: 4.071

4.  Impact of different biologically-adapted radiotherapy strategies on tumor control evaluated with a tumor response model.

Authors:  Araceli Gago-Arias; Beatriz Sánchez-Nieto; Ignacio Espinoza; Christian P Karger; Juan Pardo-Montero
Journal:  PLoS One       Date:  2018-04-26       Impact factor: 3.240

  4 in total

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