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Predictive SIRT dosimetry based on a territorial model
BACKGROUND: In the planning of selective internal radiation therapy (SIRT) for liver cancer treatment, one major aspect is to determine the prescribed activity and to estimate the resulting absorbed dose inside normal liver and tumor tissue. An optimized partition model for SIRT dosimetry based on a...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5663990/ https://www.ncbi.nlm.nih.gov/pubmed/29090358 http://dx.doi.org/10.1186/s40658-017-0192-5 |
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author | Spahr, Nadine Schilling, Philipp Thoduka, Smita Abolmaali, Nasreddin Schenk, Andrea |
author_facet | Spahr, Nadine Schilling, Philipp Thoduka, Smita Abolmaali, Nasreddin Schenk, Andrea |
author_sort | Spahr, Nadine |
collection | PubMed |
description | BACKGROUND: In the planning of selective internal radiation therapy (SIRT) for liver cancer treatment, one major aspect is to determine the prescribed activity and to estimate the resulting absorbed dose inside normal liver and tumor tissue. An optimized partition model for SIRT dosimetry based on arterial liver territories is proposed. This model is dedicated to characterize the variability of dose within the whole liver. For an arbitrary partition, the generalized absorbed dose is derived from the classical partition model. This enables to consider normal liver partitions for each arterial perfusion supply area and one partition for each tumor for activity and dose calculation. The proposed method excludes a margin of 11 mm emitting range around tumor volumes from normal liver to investigate the impact on activity calculation. Activity and dose calculation was performed for five patients using the body-surface-area (BSA) method, the classical and territorial partition model. RESULTS: The territorial model reaches smaller normal liver doses and significant higher tumor doses compared to the classical partition model. The exclusion of a small region around tumors has a significant impact on mean liver dose. Determined tumor activities for the proposed method are higher in all patients when limited by normal liver dose. Activity calculation based on BSA achieves in all cases the lowest amount. CONCLUSIONS: The territorial model provides a more local and patient-individual dose distribution in normal liver taking into account arterial supply areas. This proposed arterial liver territory-based partition model may be used for SPECT-independent activity calculation and dose prediction under the condition of an artery-based simulation for particle distribution. |
format | Online Article Text |
id | pubmed-5663990 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-56639902017-11-15 Predictive SIRT dosimetry based on a territorial model Spahr, Nadine Schilling, Philipp Thoduka, Smita Abolmaali, Nasreddin Schenk, Andrea EJNMMI Phys Original Research BACKGROUND: In the planning of selective internal radiation therapy (SIRT) for liver cancer treatment, one major aspect is to determine the prescribed activity and to estimate the resulting absorbed dose inside normal liver and tumor tissue. An optimized partition model for SIRT dosimetry based on arterial liver territories is proposed. This model is dedicated to characterize the variability of dose within the whole liver. For an arbitrary partition, the generalized absorbed dose is derived from the classical partition model. This enables to consider normal liver partitions for each arterial perfusion supply area and one partition for each tumor for activity and dose calculation. The proposed method excludes a margin of 11 mm emitting range around tumor volumes from normal liver to investigate the impact on activity calculation. Activity and dose calculation was performed for five patients using the body-surface-area (BSA) method, the classical and territorial partition model. RESULTS: The territorial model reaches smaller normal liver doses and significant higher tumor doses compared to the classical partition model. The exclusion of a small region around tumors has a significant impact on mean liver dose. Determined tumor activities for the proposed method are higher in all patients when limited by normal liver dose. Activity calculation based on BSA achieves in all cases the lowest amount. CONCLUSIONS: The territorial model provides a more local and patient-individual dose distribution in normal liver taking into account arterial supply areas. This proposed arterial liver territory-based partition model may be used for SPECT-independent activity calculation and dose prediction under the condition of an artery-based simulation for particle distribution. Springer International Publishing 2017-10-31 /pmc/articles/PMC5663990/ /pubmed/29090358 http://dx.doi.org/10.1186/s40658-017-0192-5 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Research Spahr, Nadine Schilling, Philipp Thoduka, Smita Abolmaali, Nasreddin Schenk, Andrea Predictive SIRT dosimetry based on a territorial model |
title | Predictive SIRT dosimetry based on a territorial model |
title_full | Predictive SIRT dosimetry based on a territorial model |
title_fullStr | Predictive SIRT dosimetry based on a territorial model |
title_full_unstemmed | Predictive SIRT dosimetry based on a territorial model |
title_short | Predictive SIRT dosimetry based on a territorial model |
title_sort | predictive sirt dosimetry based on a territorial model |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5663990/ https://www.ncbi.nlm.nih.gov/pubmed/29090358 http://dx.doi.org/10.1186/s40658-017-0192-5 |
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