Multiscale computational modeling of cancer growth using features derived from microCT images

Abstract Advances in medical imaging technologies now allow noninvasive image acquisition from individual patients at high spatiotemporal resolutions. A relatively new effort of predictive oncology is to develop a paradigm for forecasting the future status of an individual tumor given initial condit...

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Autores principales:	M. Hossein Zangooei, Ryan Margolis, Kenneth Hoyt
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3a4dc29bfb714af780e801bffd73c8b6
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spelling	oai:doaj.org-article:3a4dc29bfb714af780e801bffd73c8b62021-12-02T18:50:48ZMultiscale computational modeling of cancer growth using features derived from microCT images10.1038/s41598-021-97966-12045-2322https://doaj.org/article/3a4dc29bfb714af780e801bffd73c8b62021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97966-1https://doaj.org/toc/2045-2322Abstract Advances in medical imaging technologies now allow noninvasive image acquisition from individual patients at high spatiotemporal resolutions. A relatively new effort of predictive oncology is to develop a paradigm for forecasting the future status of an individual tumor given initial conditions and an appropriate mathematical model. The objective of this study was to introduce a comprehensive multiscale computational method to predict cancer and microvascular network growth patterns. A rectangular lattice-based model was designed so different evolutionary scenarios could be simulated and for predicting the impact of diffusible factors on tumor morphology and size. Further, the model allows prediction-based simulation of cell and microvascular behavior. Like a single cell, each agent is fully realized within the model and interactions are governed in part by machine learning methods. This multiscale computational model was developed and incorporated input information from in vivo microscale computed tomography (microCT) images acquired from breast cancer-bearing mice. It was found that as the difference between expansion of the cancer cell population and microvascular network increases, cells undergo proliferation and migration with a greater probability compared to other phenotypes. Overall, multiscale computational model agreed with both theoretical expectations and experimental findings (microCT images) not used during model training.M. Hossein ZangooeiRyan MargolisKenneth HoytNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q M. Hossein Zangooei Ryan Margolis Kenneth Hoyt Multiscale computational modeling of cancer growth using features derived from microCT images
description	Abstract Advances in medical imaging technologies now allow noninvasive image acquisition from individual patients at high spatiotemporal resolutions. A relatively new effort of predictive oncology is to develop a paradigm for forecasting the future status of an individual tumor given initial conditions and an appropriate mathematical model. The objective of this study was to introduce a comprehensive multiscale computational method to predict cancer and microvascular network growth patterns. A rectangular lattice-based model was designed so different evolutionary scenarios could be simulated and for predicting the impact of diffusible factors on tumor morphology and size. Further, the model allows prediction-based simulation of cell and microvascular behavior. Like a single cell, each agent is fully realized within the model and interactions are governed in part by machine learning methods. This multiscale computational model was developed and incorporated input information from in vivo microscale computed tomography (microCT) images acquired from breast cancer-bearing mice. It was found that as the difference between expansion of the cancer cell population and microvascular network increases, cells undergo proliferation and migration with a greater probability compared to other phenotypes. Overall, multiscale computational model agreed with both theoretical expectations and experimental findings (microCT images) not used during model training.
format	article
author	M. Hossein Zangooei Ryan Margolis Kenneth Hoyt
author_facet	M. Hossein Zangooei Ryan Margolis Kenneth Hoyt
author_sort	M. Hossein Zangooei
title	Multiscale computational modeling of cancer growth using features derived from microCT images
title_short	Multiscale computational modeling of cancer growth using features derived from microCT images
title_full	Multiscale computational modeling of cancer growth using features derived from microCT images
title_fullStr	Multiscale computational modeling of cancer growth using features derived from microCT images
title_full_unstemmed	Multiscale computational modeling of cancer growth using features derived from microCT images
title_sort	multiscale computational modeling of cancer growth using features derived from microct images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3a4dc29bfb714af780e801bffd73c8b6
work_keys_str_mv	AT mhosseinzangooei multiscalecomputationalmodelingofcancergrowthusingfeaturesderivedfrommicroctimages AT ryanmargolis multiscalecomputationalmodelingofcancergrowthusingfeaturesderivedfrommicroctimages AT kennethhoyt multiscalecomputationalmodelingofcancergrowthusingfeaturesderivedfrommicroctimages
_version_	1718377497472532480

Multiscale computational modeling of cancer growth using features derived from microCT images

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