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Toward Patient-Specific, Biologically Optimized Radiation Therapy Plans for the Treatment of Glioblastoma

PURPOSE: To demonstrate a method of generating patient-specific, biologically-guided radiotherapy dose plans and compare them to the standard-of-care protocol. METHODS AND MATERIALS: We integrated a patient-specific biomathematical model of glioma proliferation, invasion and radiotherapy with a mult...

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Autores principales: Corwin, David, Holdsworth, Clay, Rockne, Russell C., Trister, Andrew D., Mrugala, Maciej M., Rockhill, Jason K., Stewart, Robert D., Phillips, Mark, Swanson, Kristin R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3827144/
https://www.ncbi.nlm.nih.gov/pubmed/24265748
http://dx.doi.org/10.1371/journal.pone.0079115
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author Corwin, David
Holdsworth, Clay
Rockne, Russell C.
Trister, Andrew D.
Mrugala, Maciej M.
Rockhill, Jason K.
Stewart, Robert D.
Phillips, Mark
Swanson, Kristin R.
author_facet Corwin, David
Holdsworth, Clay
Rockne, Russell C.
Trister, Andrew D.
Mrugala, Maciej M.
Rockhill, Jason K.
Stewart, Robert D.
Phillips, Mark
Swanson, Kristin R.
author_sort Corwin, David
collection PubMed
description PURPOSE: To demonstrate a method of generating patient-specific, biologically-guided radiotherapy dose plans and compare them to the standard-of-care protocol. METHODS AND MATERIALS: We integrated a patient-specific biomathematical model of glioma proliferation, invasion and radiotherapy with a multiobjective evolutionary algorithm for intensity-modulated radiation therapy optimization to construct individualized, biologically-guided plans for 11 glioblastoma patients. Patient-individualized, spherically-symmetric simulations of the standard-of-care and optimized plans were compared in terms of several biological metrics. RESULTS: The integrated model generated spatially non-uniform doses that, when compared to the standard-of-care protocol, resulted in a 67% to 93% decrease in equivalent uniform dose to normal tissue, while the therapeutic ratio, the ratio of tumor equivalent uniform dose to that of normal tissue, increased between 50% to 265%. Applying a novel metric of treatment response (Days Gained) to the patient-individualized simulation results predicted that the optimized plans would have a significant impact on delaying tumor progression, with increases from 21% to 105% for 9 of 11 patients. CONCLUSIONS: Patient-individualized simulations using the combination of a biomathematical model with an optimization algorithm for radiation therapy generated biologically-guided doses that decreased normal tissue EUD and increased therapeutic ratio with the potential to improve survival outcomes for treatment of glioblastoma.
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spelling pubmed-38271442013-11-21 Toward Patient-Specific, Biologically Optimized Radiation Therapy Plans for the Treatment of Glioblastoma Corwin, David Holdsworth, Clay Rockne, Russell C. Trister, Andrew D. Mrugala, Maciej M. Rockhill, Jason K. Stewart, Robert D. Phillips, Mark Swanson, Kristin R. PLoS One Research Article PURPOSE: To demonstrate a method of generating patient-specific, biologically-guided radiotherapy dose plans and compare them to the standard-of-care protocol. METHODS AND MATERIALS: We integrated a patient-specific biomathematical model of glioma proliferation, invasion and radiotherapy with a multiobjective evolutionary algorithm for intensity-modulated radiation therapy optimization to construct individualized, biologically-guided plans for 11 glioblastoma patients. Patient-individualized, spherically-symmetric simulations of the standard-of-care and optimized plans were compared in terms of several biological metrics. RESULTS: The integrated model generated spatially non-uniform doses that, when compared to the standard-of-care protocol, resulted in a 67% to 93% decrease in equivalent uniform dose to normal tissue, while the therapeutic ratio, the ratio of tumor equivalent uniform dose to that of normal tissue, increased between 50% to 265%. Applying a novel metric of treatment response (Days Gained) to the patient-individualized simulation results predicted that the optimized plans would have a significant impact on delaying tumor progression, with increases from 21% to 105% for 9 of 11 patients. CONCLUSIONS: Patient-individualized simulations using the combination of a biomathematical model with an optimization algorithm for radiation therapy generated biologically-guided doses that decreased normal tissue EUD and increased therapeutic ratio with the potential to improve survival outcomes for treatment of glioblastoma. Public Library of Science 2013-11-12 /pmc/articles/PMC3827144/ /pubmed/24265748 http://dx.doi.org/10.1371/journal.pone.0079115 Text en © 2013 Corwin et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Corwin, David
Holdsworth, Clay
Rockne, Russell C.
Trister, Andrew D.
Mrugala, Maciej M.
Rockhill, Jason K.
Stewart, Robert D.
Phillips, Mark
Swanson, Kristin R.
Toward Patient-Specific, Biologically Optimized Radiation Therapy Plans for the Treatment of Glioblastoma
title Toward Patient-Specific, Biologically Optimized Radiation Therapy Plans for the Treatment of Glioblastoma
title_full Toward Patient-Specific, Biologically Optimized Radiation Therapy Plans for the Treatment of Glioblastoma
title_fullStr Toward Patient-Specific, Biologically Optimized Radiation Therapy Plans for the Treatment of Glioblastoma
title_full_unstemmed Toward Patient-Specific, Biologically Optimized Radiation Therapy Plans for the Treatment of Glioblastoma
title_short Toward Patient-Specific, Biologically Optimized Radiation Therapy Plans for the Treatment of Glioblastoma
title_sort toward patient-specific, biologically optimized radiation therapy plans for the treatment of glioblastoma
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3827144/
https://www.ncbi.nlm.nih.gov/pubmed/24265748
http://dx.doi.org/10.1371/journal.pone.0079115
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