Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation

Abstract High-grade gliomas are an aggressive and invasive malignancy which are susceptible to treatment resistance due to heterogeneity in intratumoral properties such as cell proliferation and density and perfusion. Non-invasive imaging approaches can measure these properties, which can then be us...

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Autores principales:	David A. Hormuth, Karine A. Al Feghali, Andrew M. Elliott, Thomas E. Yankeelov, Caroline Chung
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/55eb86d0b3a0467cb8f80d1460fdead2
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spelling	oai:doaj.org-article:55eb86d0b3a0467cb8f80d1460fdead22021-12-02T13:39:47ZImage-based personalization of computational models for predicting response of high-grade glioma to chemoradiation10.1038/s41598-021-87887-42045-2322https://doaj.org/article/55eb86d0b3a0467cb8f80d1460fdead22021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87887-4https://doaj.org/toc/2045-2322Abstract High-grade gliomas are an aggressive and invasive malignancy which are susceptible to treatment resistance due to heterogeneity in intratumoral properties such as cell proliferation and density and perfusion. Non-invasive imaging approaches can measure these properties, which can then be used to calibrate patient-specific mathematical models of tumor growth and response. We employed multiparametric magnetic resonance imaging (MRI) to identify tumor extent (via contrast-enhanced T 1 -weighted, and T 2 -FLAIR) and capture intratumoral heterogeneity in cell density (via diffusion-weighted imaging) to calibrate a family of mathematical models of chemoradiation response in nine patients with unresected or partially resected disease. The calibrated model parameters were used to forecast spatially-mapped individual tumor response at future imaging visits. We then employed the Akaike information criteria to select the most parsimonious member from the family, a novel two-species model describing the enhancing and non-enhancing components of the tumor. Using this model, we achieved low error in predictions of the enhancing volume (median: − 2.5%, interquartile range: 10.0%) and a strong correlation in total cell count (Kendall correlation coefficient 0.79) at 3-months post-treatment. These preliminary results demonstrate the plausibility of using multiparametric MRI data to inform spatially-informative, biologically-based predictive models of tumor response in the setting of clinical high-grade gliomas.David A. HormuthKarine A. Al FeghaliAndrew M. ElliottThomas E. YankeelovCaroline ChungNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q David A. Hormuth Karine A. Al Feghali Andrew M. Elliott Thomas E. Yankeelov Caroline Chung Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation
description	Abstract High-grade gliomas are an aggressive and invasive malignancy which are susceptible to treatment resistance due to heterogeneity in intratumoral properties such as cell proliferation and density and perfusion. Non-invasive imaging approaches can measure these properties, which can then be used to calibrate patient-specific mathematical models of tumor growth and response. We employed multiparametric magnetic resonance imaging (MRI) to identify tumor extent (via contrast-enhanced T 1 -weighted, and T 2 -FLAIR) and capture intratumoral heterogeneity in cell density (via diffusion-weighted imaging) to calibrate a family of mathematical models of chemoradiation response in nine patients with unresected or partially resected disease. The calibrated model parameters were used to forecast spatially-mapped individual tumor response at future imaging visits. We then employed the Akaike information criteria to select the most parsimonious member from the family, a novel two-species model describing the enhancing and non-enhancing components of the tumor. Using this model, we achieved low error in predictions of the enhancing volume (median: − 2.5%, interquartile range: 10.0%) and a strong correlation in total cell count (Kendall correlation coefficient 0.79) at 3-months post-treatment. These preliminary results demonstrate the plausibility of using multiparametric MRI data to inform spatially-informative, biologically-based predictive models of tumor response in the setting of clinical high-grade gliomas.
format	article
author	David A. Hormuth Karine A. Al Feghali Andrew M. Elliott Thomas E. Yankeelov Caroline Chung
author_facet	David A. Hormuth Karine A. Al Feghali Andrew M. Elliott Thomas E. Yankeelov Caroline Chung
author_sort	David A. Hormuth
title	Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation
title_short	Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation
title_full	Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation
title_fullStr	Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation
title_full_unstemmed	Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation
title_sort	image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/55eb86d0b3a0467cb8f80d1460fdead2
work_keys_str_mv	AT davidahormuth imagebasedpersonalizationofcomputationalmodelsforpredictingresponseofhighgradegliomatochemoradiation AT karineaalfeghali imagebasedpersonalizationofcomputationalmodelsforpredictingresponseofhighgradegliomatochemoradiation AT andrewmelliott imagebasedpersonalizationofcomputationalmodelsforpredictingresponseofhighgradegliomatochemoradiation AT thomaseyankeelov imagebasedpersonalizationofcomputationalmodelsforpredictingresponseofhighgradegliomatochemoradiation AT carolinechung imagebasedpersonalizationofcomputationalmodelsforpredictingresponseofhighgradegliomatochemoradiation
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Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation

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