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New target volume delineation and PTV strategies to further personalise radiotherapy

Target volume delineation uncertainty (DU) is arguably one of the largest geometric uncertainties in radiotherapy that are accounted for using planning target volume (PTV) margins. Geometrical uncertainties are typically derived from a limited sample of patients. Consequently, the resultant margins...

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Autores principales: Bernstein, David, Taylor, Alexandra, Nill, Simeon, Oelfke, Uwe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOP Publishing 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8208617/
https://www.ncbi.nlm.nih.gov/pubmed/33498018
http://dx.doi.org/10.1088/1361-6560/abe029
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author Bernstein, David
Taylor, Alexandra
Nill, Simeon
Oelfke, Uwe
author_facet Bernstein, David
Taylor, Alexandra
Nill, Simeon
Oelfke, Uwe
author_sort Bernstein, David
collection PubMed
description Target volume delineation uncertainty (DU) is arguably one of the largest geometric uncertainties in radiotherapy that are accounted for using planning target volume (PTV) margins. Geometrical uncertainties are typically derived from a limited sample of patients. Consequently, the resultant margins are not tailored to individual patients. Furthermore, standard PTVs cannot account for arbitrary anisotropic extensions of the target volume originating from DU. We address these limitations by developing a method to measure DU for each patient by a single clinician. This information is then used to produce PTVs that account for each patient’s unique DU, including any required anisotropic component. We do so using a two-step uncertainty evaluation strategy that does not rely on multiple samples of data to capture the DU of a patient’s gross tumour volume (GTV) or clinical target volume. For simplicity, we will just refer to the GTV in the following. First, the clinician delineates two contour sets; one which bounds all voxels believed to have a probability of belonging to the GTV of 1, while the second includes all voxels with a probability greater than 0. Next, one specifies a probability density function for the true GTV boundary position within the boundaries of the two contours. Finally, a patient-specific PTV, designed to account for all systematic errors, is created using this information along with measurements of the other systematic errors. Clinical examples indicate that our margin strategy can produce significantly smaller PTVs than the van Herk margin recipe. Our new radiotherapy target delineation concept allows DUs to be quantified by the clinician for each patient, leading to PTV margins that are tailored to each unique patient, thus paving the way to a greater personalisation of radiotherapy.
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spelling pubmed-82086172021-06-17 New target volume delineation and PTV strategies to further personalise radiotherapy Bernstein, David Taylor, Alexandra Nill, Simeon Oelfke, Uwe Phys Med Biol Paper Target volume delineation uncertainty (DU) is arguably one of the largest geometric uncertainties in radiotherapy that are accounted for using planning target volume (PTV) margins. Geometrical uncertainties are typically derived from a limited sample of patients. Consequently, the resultant margins are not tailored to individual patients. Furthermore, standard PTVs cannot account for arbitrary anisotropic extensions of the target volume originating from DU. We address these limitations by developing a method to measure DU for each patient by a single clinician. This information is then used to produce PTVs that account for each patient’s unique DU, including any required anisotropic component. We do so using a two-step uncertainty evaluation strategy that does not rely on multiple samples of data to capture the DU of a patient’s gross tumour volume (GTV) or clinical target volume. For simplicity, we will just refer to the GTV in the following. First, the clinician delineates two contour sets; one which bounds all voxels believed to have a probability of belonging to the GTV of 1, while the second includes all voxels with a probability greater than 0. Next, one specifies a probability density function for the true GTV boundary position within the boundaries of the two contours. Finally, a patient-specific PTV, designed to account for all systematic errors, is created using this information along with measurements of the other systematic errors. Clinical examples indicate that our margin strategy can produce significantly smaller PTVs than the van Herk margin recipe. Our new radiotherapy target delineation concept allows DUs to be quantified by the clinician for each patient, leading to PTV margins that are tailored to each unique patient, thus paving the way to a greater personalisation of radiotherapy. IOP Publishing 2021-03-07 2021-02-25 /pmc/articles/PMC8208617/ /pubmed/33498018 http://dx.doi.org/10.1088/1361-6560/abe029 Text en © 2021 The Author(s). Published on behalf of Institute of Physics and Engineering in Medicine by IOP Publishing Ltd https://creativecommons.org/licenses/by/4.0/Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence (https://creativecommons.org/licenses/by/4.0/) . Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
spellingShingle Paper
Bernstein, David
Taylor, Alexandra
Nill, Simeon
Oelfke, Uwe
New target volume delineation and PTV strategies to further personalise radiotherapy
title New target volume delineation and PTV strategies to further personalise radiotherapy
title_full New target volume delineation and PTV strategies to further personalise radiotherapy
title_fullStr New target volume delineation and PTV strategies to further personalise radiotherapy
title_full_unstemmed New target volume delineation and PTV strategies to further personalise radiotherapy
title_short New target volume delineation and PTV strategies to further personalise radiotherapy
title_sort new target volume delineation and ptv strategies to further personalise radiotherapy
topic Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8208617/
https://www.ncbi.nlm.nih.gov/pubmed/33498018
http://dx.doi.org/10.1088/1361-6560/abe029
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