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Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data

SIMPLE SUMMARY: The recruitment of new vasculature via angiogenesis is a critical component of tumor development, which fundamentally influences tumor growth and response to treatment. The characterization of tumor-induced angiogenesis via mathematical models could enable approaches to forecast tumo...

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Autores principales: Hormuth, David A., Phillips, Caleb M., Wu, Chengyue, Lima, Ernesto A. B. F., Lorenzo, Guillermo, Jha, Prashant K., Jarrett, Angela M., Oden, J. Tinsley, Yankeelov, Thomas E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8234316/
https://www.ncbi.nlm.nih.gov/pubmed/34208448
http://dx.doi.org/10.3390/cancers13123008
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author Hormuth, David A.
Phillips, Caleb M.
Wu, Chengyue
Lima, Ernesto A. B. F.
Lorenzo, Guillermo
Jha, Prashant K.
Jarrett, Angela M.
Oden, J. Tinsley
Yankeelov, Thomas E.
author_facet Hormuth, David A.
Phillips, Caleb M.
Wu, Chengyue
Lima, Ernesto A. B. F.
Lorenzo, Guillermo
Jha, Prashant K.
Jarrett, Angela M.
Oden, J. Tinsley
Yankeelov, Thomas E.
author_sort Hormuth, David A.
collection PubMed
description SIMPLE SUMMARY: The recruitment of new vasculature via angiogenesis is a critical component of tumor development, which fundamentally influences tumor growth and response to treatment. The characterization of tumor-induced angiogenesis via mathematical models could enable approaches to forecast tumor response and improve patient care. In this review, we discuss how time-resolved imaging data integrated with mathematical modeling can be used to systematically investigate angiogenesis from the cell to tissue scale and ultimately forecast response to therapy. ABSTRACT: Tumor-associated vasculature is responsible for the delivery of nutrients, removal of waste, and allowing growth beyond 2–3 mm(3). Additionally, the vascular network, which is changing in both space and time, fundamentally influences tumor response to both systemic and radiation therapy. Thus, a robust understanding of vascular dynamics is necessary to accurately predict tumor growth, as well as establish optimal treatment protocols to achieve optimal tumor control. Such a goal requires the intimate integration of both theory and experiment. Quantitative and time-resolved imaging methods have emerged as technologies able to visualize and characterize tumor vascular properties before and during therapy at the tissue and cell scale. Parallel to, but separate from those developments, mathematical modeling techniques have been developed to enable in silico investigations into theoretical tumor and vascular dynamics. In particular, recent efforts have sought to integrate both theory and experiment to enable data-driven mathematical modeling. Such mathematical models are calibrated by data obtained from individual tumor-vascular systems to predict future vascular growth, delivery of systemic agents, and response to radiotherapy. In this review, we discuss experimental techniques for visualizing and quantifying vascular dynamics including magnetic resonance imaging, microfluidic devices, and confocal microscopy. We then focus on the integration of these experimental measures with biologically based mathematical models to generate testable predictions.
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spelling pubmed-82343162021-06-27 Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data Hormuth, David A. Phillips, Caleb M. Wu, Chengyue Lima, Ernesto A. B. F. Lorenzo, Guillermo Jha, Prashant K. Jarrett, Angela M. Oden, J. Tinsley Yankeelov, Thomas E. Cancers (Basel) Review SIMPLE SUMMARY: The recruitment of new vasculature via angiogenesis is a critical component of tumor development, which fundamentally influences tumor growth and response to treatment. The characterization of tumor-induced angiogenesis via mathematical models could enable approaches to forecast tumor response and improve patient care. In this review, we discuss how time-resolved imaging data integrated with mathematical modeling can be used to systematically investigate angiogenesis from the cell to tissue scale and ultimately forecast response to therapy. ABSTRACT: Tumor-associated vasculature is responsible for the delivery of nutrients, removal of waste, and allowing growth beyond 2–3 mm(3). Additionally, the vascular network, which is changing in both space and time, fundamentally influences tumor response to both systemic and radiation therapy. Thus, a robust understanding of vascular dynamics is necessary to accurately predict tumor growth, as well as establish optimal treatment protocols to achieve optimal tumor control. Such a goal requires the intimate integration of both theory and experiment. Quantitative and time-resolved imaging methods have emerged as technologies able to visualize and characterize tumor vascular properties before and during therapy at the tissue and cell scale. Parallel to, but separate from those developments, mathematical modeling techniques have been developed to enable in silico investigations into theoretical tumor and vascular dynamics. In particular, recent efforts have sought to integrate both theory and experiment to enable data-driven mathematical modeling. Such mathematical models are calibrated by data obtained from individual tumor-vascular systems to predict future vascular growth, delivery of systemic agents, and response to radiotherapy. In this review, we discuss experimental techniques for visualizing and quantifying vascular dynamics including magnetic resonance imaging, microfluidic devices, and confocal microscopy. We then focus on the integration of these experimental measures with biologically based mathematical models to generate testable predictions. MDPI 2021-06-16 /pmc/articles/PMC8234316/ /pubmed/34208448 http://dx.doi.org/10.3390/cancers13123008 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Hormuth, David A.
Phillips, Caleb M.
Wu, Chengyue
Lima, Ernesto A. B. F.
Lorenzo, Guillermo
Jha, Prashant K.
Jarrett, Angela M.
Oden, J. Tinsley
Yankeelov, Thomas E.
Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data
title Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data
title_full Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data
title_fullStr Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data
title_full_unstemmed Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data
title_short Biologically-Based Mathematical Modeling of Tumor Vasculature and Angiogenesis via Time-Resolved Imaging Data
title_sort biologically-based mathematical modeling of tumor vasculature and angiogenesis via time-resolved imaging data
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8234316/
https://www.ncbi.nlm.nih.gov/pubmed/34208448
http://dx.doi.org/10.3390/cancers13123008
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