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Model-Supported Radiotherapy Personalization: In silico Test of Hyper- and Hypo-Fractionation Effects
The need for radiotherapy personalization is now widely recognized, however, it would require considerations not only on the probability of control and survival of the tumor, but also on the possible toxic effects, on the quality of the expected life and the economic efficiency of the treatment. In...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6197078/ https://www.ncbi.nlm.nih.gov/pubmed/30374310 http://dx.doi.org/10.3389/fphys.2018.01445 |
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author | Belfatto, Antonella Jereczek-Fossa, Barbara Alicja Baroni, Guido Cerveri, Pietro |
author_facet | Belfatto, Antonella Jereczek-Fossa, Barbara Alicja Baroni, Guido Cerveri, Pietro |
author_sort | Belfatto, Antonella |
collection | PubMed |
description | The need for radiotherapy personalization is now widely recognized, however, it would require considerations not only on the probability of control and survival of the tumor, but also on the possible toxic effects, on the quality of the expected life and the economic efficiency of the treatment. In this paper, we propose a simulation tool that can be integrated into a decision support system that allows selection of the most suitable irradiation regimen. We used a macroscale mathematical model, which includes active and necrotic tumor dynamics and the role of oxygenation to simulate the effects of different hypo-/hyper-fractional regimens using retrospective data of seven virtual patients from as many cervical cancer patients used for its training in a previous study. The results confirmed the heterogeneous response across the patients as a function of treatment regimen and suggested the tumor growth rate as a main factor in the final tumor regression. In addition to the maximum regression, another criterion was suggested to select the most suitable regimen (minimum number of fractions to achieve a regression of 80%) minimizing the toxicity and maximizing the cost-effectiveness ratio. Despite the lack of direct validation, the simulation results are in agreement with the literature findings that suggest the need for hypo-fractionated regimens in case of aggressive tumor phenotypes. Finally, the paper suggests a possible exploitation of the model within a tool to support clinical decisions. |
format | Online Article Text |
id | pubmed-6197078 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-61970782018-10-29 Model-Supported Radiotherapy Personalization: In silico Test of Hyper- and Hypo-Fractionation Effects Belfatto, Antonella Jereczek-Fossa, Barbara Alicja Baroni, Guido Cerveri, Pietro Front Physiol Physiology The need for radiotherapy personalization is now widely recognized, however, it would require considerations not only on the probability of control and survival of the tumor, but also on the possible toxic effects, on the quality of the expected life and the economic efficiency of the treatment. In this paper, we propose a simulation tool that can be integrated into a decision support system that allows selection of the most suitable irradiation regimen. We used a macroscale mathematical model, which includes active and necrotic tumor dynamics and the role of oxygenation to simulate the effects of different hypo-/hyper-fractional regimens using retrospective data of seven virtual patients from as many cervical cancer patients used for its training in a previous study. The results confirmed the heterogeneous response across the patients as a function of treatment regimen and suggested the tumor growth rate as a main factor in the final tumor regression. In addition to the maximum regression, another criterion was suggested to select the most suitable regimen (minimum number of fractions to achieve a regression of 80%) minimizing the toxicity and maximizing the cost-effectiveness ratio. Despite the lack of direct validation, the simulation results are in agreement with the literature findings that suggest the need for hypo-fractionated regimens in case of aggressive tumor phenotypes. Finally, the paper suggests a possible exploitation of the model within a tool to support clinical decisions. Frontiers Media S.A. 2018-10-15 /pmc/articles/PMC6197078/ /pubmed/30374310 http://dx.doi.org/10.3389/fphys.2018.01445 Text en Copyright © 2018 Belfatto, Jereczek-Fossa, Baroni and Cerveri. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Physiology Belfatto, Antonella Jereczek-Fossa, Barbara Alicja Baroni, Guido Cerveri, Pietro Model-Supported Radiotherapy Personalization: In silico Test of Hyper- and Hypo-Fractionation Effects |
title | Model-Supported Radiotherapy Personalization: In silico Test of Hyper- and Hypo-Fractionation Effects |
title_full | Model-Supported Radiotherapy Personalization: In silico Test of Hyper- and Hypo-Fractionation Effects |
title_fullStr | Model-Supported Radiotherapy Personalization: In silico Test of Hyper- and Hypo-Fractionation Effects |
title_full_unstemmed | Model-Supported Radiotherapy Personalization: In silico Test of Hyper- and Hypo-Fractionation Effects |
title_short | Model-Supported Radiotherapy Personalization: In silico Test of Hyper- and Hypo-Fractionation Effects |
title_sort | model-supported radiotherapy personalization: in silico test of hyper- and hypo-fractionation effects |
topic | Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6197078/ https://www.ncbi.nlm.nih.gov/pubmed/30374310 http://dx.doi.org/10.3389/fphys.2018.01445 |
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