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Accurate state estimation from uncertain data and models: an application of data assimilation to mathematical models of human brain tumors

BACKGROUND: Data assimilation refers to methods for updating the state vector (initial condition) of a complex spatiotemporal model (such as a numerical weather model) by combining new observations with one or more prior forecasts. We consider the potential feasibility of this approach for making sh...

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Autores principales: Kostelich, Eric J, Kuang, Yang, McDaniel, Joshua M, Moore, Nina Z, Martirosyan, Nikolay L, Preul, Mark C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3340325/
https://www.ncbi.nlm.nih.gov/pubmed/22185645
http://dx.doi.org/10.1186/1745-6150-6-64
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author Kostelich, Eric J
Kuang, Yang
McDaniel, Joshua M
Moore, Nina Z
Martirosyan, Nikolay L
Preul, Mark C
author_facet Kostelich, Eric J
Kuang, Yang
McDaniel, Joshua M
Moore, Nina Z
Martirosyan, Nikolay L
Preul, Mark C
author_sort Kostelich, Eric J
collection PubMed
description BACKGROUND: Data assimilation refers to methods for updating the state vector (initial condition) of a complex spatiotemporal model (such as a numerical weather model) by combining new observations with one or more prior forecasts. We consider the potential feasibility of this approach for making short-term (60-day) forecasts of the growth and spread of a malignant brain cancer (glioblastoma multiforme) in individual patient cases, where the observations are synthetic magnetic resonance images of a hypothetical tumor. RESULTS: We apply a modern state estimation algorithm (the Local Ensemble Transform Kalman Filter), previously developed for numerical weather prediction, to two different mathematical models of glioblastoma, taking into account likely errors in model parameters and measurement uncertainties in magnetic resonance imaging. The filter can accurately shadow the growth of a representative synthetic tumor for 360 days (six 60-day forecast/update cycles) in the presence of a moderate degree of systematic model error and measurement noise. CONCLUSIONS: The mathematical methodology described here may prove useful for other modeling efforts in biology and oncology. An accurate forecast system for glioblastoma may prove useful in clinical settings for treatment planning and patient counseling. REVIEWERS: This article was reviewed by Anthony Almudevar, Tomas Radivoyevitch, and Kristin Swanson (nominated by Georg Luebeck).
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spelling pubmed-33403252012-05-01 Accurate state estimation from uncertain data and models: an application of data assimilation to mathematical models of human brain tumors Kostelich, Eric J Kuang, Yang McDaniel, Joshua M Moore, Nina Z Martirosyan, Nikolay L Preul, Mark C Biol Direct Research BACKGROUND: Data assimilation refers to methods for updating the state vector (initial condition) of a complex spatiotemporal model (such as a numerical weather model) by combining new observations with one or more prior forecasts. We consider the potential feasibility of this approach for making short-term (60-day) forecasts of the growth and spread of a malignant brain cancer (glioblastoma multiforme) in individual patient cases, where the observations are synthetic magnetic resonance images of a hypothetical tumor. RESULTS: We apply a modern state estimation algorithm (the Local Ensemble Transform Kalman Filter), previously developed for numerical weather prediction, to two different mathematical models of glioblastoma, taking into account likely errors in model parameters and measurement uncertainties in magnetic resonance imaging. The filter can accurately shadow the growth of a representative synthetic tumor for 360 days (six 60-day forecast/update cycles) in the presence of a moderate degree of systematic model error and measurement noise. CONCLUSIONS: The mathematical methodology described here may prove useful for other modeling efforts in biology and oncology. An accurate forecast system for glioblastoma may prove useful in clinical settings for treatment planning and patient counseling. REVIEWERS: This article was reviewed by Anthony Almudevar, Tomas Radivoyevitch, and Kristin Swanson (nominated by Georg Luebeck). BioMed Central 2011-12-21 /pmc/articles/PMC3340325/ /pubmed/22185645 http://dx.doi.org/10.1186/1745-6150-6-64 Text en Copyright ©2011 Kostelich et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Kostelich, Eric J
Kuang, Yang
McDaniel, Joshua M
Moore, Nina Z
Martirosyan, Nikolay L
Preul, Mark C
Accurate state estimation from uncertain data and models: an application of data assimilation to mathematical models of human brain tumors
title Accurate state estimation from uncertain data and models: an application of data assimilation to mathematical models of human brain tumors
title_full Accurate state estimation from uncertain data and models: an application of data assimilation to mathematical models of human brain tumors
title_fullStr Accurate state estimation from uncertain data and models: an application of data assimilation to mathematical models of human brain tumors
title_full_unstemmed Accurate state estimation from uncertain data and models: an application of data assimilation to mathematical models of human brain tumors
title_short Accurate state estimation from uncertain data and models: an application of data assimilation to mathematical models of human brain tumors
title_sort accurate state estimation from uncertain data and models: an application of data assimilation to mathematical models of human brain tumors
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3340325/
https://www.ncbi.nlm.nih.gov/pubmed/22185645
http://dx.doi.org/10.1186/1745-6150-6-64
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