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Development of a mathematical model to estimate intra-tumor oxygen concentrations through multi-parametric imaging

BACKGROUND: Tumor hypoxia is involved in every stage of solid tumor development: formation, progression, metastasis, and apoptosis. Two types of hypoxia exist in tumors—chronic hypoxia and acute hypoxia. Recent studies indicate that the regional hypoxia kinetics is closely linked to metastasis and t...

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Autores principales: Lee, Chung-Wein, Stantz, Keith M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5062945/
https://www.ncbi.nlm.nih.gov/pubmed/27733170
http://dx.doi.org/10.1186/s12938-016-0235-5
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author Lee, Chung-Wein
Stantz, Keith M.
author_facet Lee, Chung-Wein
Stantz, Keith M.
author_sort Lee, Chung-Wein
collection PubMed
description BACKGROUND: Tumor hypoxia is involved in every stage of solid tumor development: formation, progression, metastasis, and apoptosis. Two types of hypoxia exist in tumors—chronic hypoxia and acute hypoxia. Recent studies indicate that the regional hypoxia kinetics is closely linked to metastasis and therapeutic responses, but regional hypoxia kinetics is hard to measure. We propose a novel approach to determine the local pO(2) by fusing the parameters obtained from in vivo functional imaging through the use of a modified multivariate Krogh model. METHODS: To test our idea and its potential to translate into an in vivo setting through the use of existing imaging techniques, simulation studies were performed comparing the local partial oxygen pressure (pO(2)) from the proposed multivariate image fusion model to the referenced pO(2) derived by Green’s function, which considers the contribution from every vessel segment of an entire three-dimensional tumor vasculature to profile tumor oxygen with high spatial resolution. RESULTS: pO(2) derived from our fusion approach were close to the referenced pO(2) with regression slope near 1.0 and an r(2) higher than 0.8 if the voxel size (or the spatial resolution set by functional imaging modality) was less than 200 μm. The simulation also showed that the metabolic rate, blood perfusion, and hemoglobin concentration were dominant factors in tissue oxygenation. The impact of the measurement error of functional imaging to the pO(2) precision and accuracy was simulated. A Gaussian error function with FWHM equal to 20 % of blood perfusion or fractional vascular volume measurement contributed to average 7 % statistical error in pO(2). CONCLUSION: The simulation results indicate that the fusion of multiple parametric maps through the biophysically derived mathematical models can monitor the intra-tumor spatial variations of hypoxia in tumors with existing imaging methods, and the potential to further investigate different forms of hypoxia, such as chronic and acute hypoxia, in response to cancer therapies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12938-016-0235-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-50629452016-10-24 Development of a mathematical model to estimate intra-tumor oxygen concentrations through multi-parametric imaging Lee, Chung-Wein Stantz, Keith M. Biomed Eng Online Research BACKGROUND: Tumor hypoxia is involved in every stage of solid tumor development: formation, progression, metastasis, and apoptosis. Two types of hypoxia exist in tumors—chronic hypoxia and acute hypoxia. Recent studies indicate that the regional hypoxia kinetics is closely linked to metastasis and therapeutic responses, but regional hypoxia kinetics is hard to measure. We propose a novel approach to determine the local pO(2) by fusing the parameters obtained from in vivo functional imaging through the use of a modified multivariate Krogh model. METHODS: To test our idea and its potential to translate into an in vivo setting through the use of existing imaging techniques, simulation studies were performed comparing the local partial oxygen pressure (pO(2)) from the proposed multivariate image fusion model to the referenced pO(2) derived by Green’s function, which considers the contribution from every vessel segment of an entire three-dimensional tumor vasculature to profile tumor oxygen with high spatial resolution. RESULTS: pO(2) derived from our fusion approach were close to the referenced pO(2) with regression slope near 1.0 and an r(2) higher than 0.8 if the voxel size (or the spatial resolution set by functional imaging modality) was less than 200 μm. The simulation also showed that the metabolic rate, blood perfusion, and hemoglobin concentration were dominant factors in tissue oxygenation. The impact of the measurement error of functional imaging to the pO(2) precision and accuracy was simulated. A Gaussian error function with FWHM equal to 20 % of blood perfusion or fractional vascular volume measurement contributed to average 7 % statistical error in pO(2). CONCLUSION: The simulation results indicate that the fusion of multiple parametric maps through the biophysically derived mathematical models can monitor the intra-tumor spatial variations of hypoxia in tumors with existing imaging methods, and the potential to further investigate different forms of hypoxia, such as chronic and acute hypoxia, in response to cancer therapies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12938-016-0235-5) contains supplementary material, which is available to authorized users. BioMed Central 2016-10-12 /pmc/articles/PMC5062945/ /pubmed/27733170 http://dx.doi.org/10.1186/s12938-016-0235-5 Text en © The Author(s) 2016 Open AccessThis 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. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Lee, Chung-Wein
Stantz, Keith M.
Development of a mathematical model to estimate intra-tumor oxygen concentrations through multi-parametric imaging
title Development of a mathematical model to estimate intra-tumor oxygen concentrations through multi-parametric imaging
title_full Development of a mathematical model to estimate intra-tumor oxygen concentrations through multi-parametric imaging
title_fullStr Development of a mathematical model to estimate intra-tumor oxygen concentrations through multi-parametric imaging
title_full_unstemmed Development of a mathematical model to estimate intra-tumor oxygen concentrations through multi-parametric imaging
title_short Development of a mathematical model to estimate intra-tumor oxygen concentrations through multi-parametric imaging
title_sort development of a mathematical model to estimate intra-tumor oxygen concentrations through multi-parametric imaging
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5062945/
https://www.ncbi.nlm.nih.gov/pubmed/27733170
http://dx.doi.org/10.1186/s12938-016-0235-5
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