Classical mathematical models for description and prediction of experimental tumor growth.

Despite internal complexity, tumor growth kinetics follow relatively simple laws that can be expressed as mathematical models. To explore this further, quantitative analysis of the most classical of these were performed. The models were assessed against data from two in vivo experimental systems: an...

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Autores principales:	Sébastien Benzekry, Clare Lamont, Afshin Beheshti, Amanda Tracz, John M L Ebos, Lynn Hlatky, Philip Hahnfeldt
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/19e31e19343449d79eb789570472c361
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spelling	oai:doaj.org-article:19e31e19343449d79eb789570472c3612021-11-25T05:40:48ZClassical mathematical models for description and prediction of experimental tumor growth.1553-734X1553-735810.1371/journal.pcbi.1003800https://doaj.org/article/19e31e19343449d79eb789570472c3612014-08-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25167199/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Despite internal complexity, tumor growth kinetics follow relatively simple laws that can be expressed as mathematical models. To explore this further, quantitative analysis of the most classical of these were performed. The models were assessed against data from two in vivo experimental systems: an ectopic syngeneic tumor (Lewis lung carcinoma) and an orthotopically xenografted human breast carcinoma. The goals were threefold: 1) to determine a statistical model for description of the measurement error, 2) to establish the descriptive power of each model, using several goodness-of-fit metrics and a study of parametric identifiability, and 3) to assess the models' ability to forecast future tumor growth. The models included in the study comprised the exponential, exponential-linear, power law, Gompertz, logistic, generalized logistic, von Bertalanffy and a model with dynamic carrying capacity. For the breast data, the dynamics were best captured by the Gompertz and exponential-linear models. The latter also exhibited the highest predictive power, with excellent prediction scores (≥80%) extending out as far as 12 days in the future. For the lung data, the Gompertz and power law models provided the most parsimonious and parametrically identifiable description. However, not one of the models was able to achieve a substantial prediction rate (≥70%) beyond the next day data point. In this context, adjunction of a priori information on the parameter distribution led to considerable improvement. For instance, forecast success rates went from 14.9% to 62.7% when using the power law model to predict the full future tumor growth curves, using just three data points. These results not only have important implications for biological theories of tumor growth and the use of mathematical modeling in preclinical anti-cancer drug investigations, but also may assist in defining how mathematical models could serve as potential prognostic tools in the clinic.Sébastien BenzekryClare LamontAfshin BeheshtiAmanda TraczJohn M L EbosLynn HlatkyPhilip HahnfeldtPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 10, Iss 8, p e1003800 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Sébastien Benzekry Clare Lamont Afshin Beheshti Amanda Tracz John M L Ebos Lynn Hlatky Philip Hahnfeldt Classical mathematical models for description and prediction of experimental tumor growth.
description	Despite internal complexity, tumor growth kinetics follow relatively simple laws that can be expressed as mathematical models. To explore this further, quantitative analysis of the most classical of these were performed. The models were assessed against data from two in vivo experimental systems: an ectopic syngeneic tumor (Lewis lung carcinoma) and an orthotopically xenografted human breast carcinoma. The goals were threefold: 1) to determine a statistical model for description of the measurement error, 2) to establish the descriptive power of each model, using several goodness-of-fit metrics and a study of parametric identifiability, and 3) to assess the models' ability to forecast future tumor growth. The models included in the study comprised the exponential, exponential-linear, power law, Gompertz, logistic, generalized logistic, von Bertalanffy and a model with dynamic carrying capacity. For the breast data, the dynamics were best captured by the Gompertz and exponential-linear models. The latter also exhibited the highest predictive power, with excellent prediction scores (≥80%) extending out as far as 12 days in the future. For the lung data, the Gompertz and power law models provided the most parsimonious and parametrically identifiable description. However, not one of the models was able to achieve a substantial prediction rate (≥70%) beyond the next day data point. In this context, adjunction of a priori information on the parameter distribution led to considerable improvement. For instance, forecast success rates went from 14.9% to 62.7% when using the power law model to predict the full future tumor growth curves, using just three data points. These results not only have important implications for biological theories of tumor growth and the use of mathematical modeling in preclinical anti-cancer drug investigations, but also may assist in defining how mathematical models could serve as potential prognostic tools in the clinic.
format	article
author	Sébastien Benzekry Clare Lamont Afshin Beheshti Amanda Tracz John M L Ebos Lynn Hlatky Philip Hahnfeldt
author_facet	Sébastien Benzekry Clare Lamont Afshin Beheshti Amanda Tracz John M L Ebos Lynn Hlatky Philip Hahnfeldt
author_sort	Sébastien Benzekry
title	Classical mathematical models for description and prediction of experimental tumor growth.
title_short	Classical mathematical models for description and prediction of experimental tumor growth.
title_full	Classical mathematical models for description and prediction of experimental tumor growth.
title_fullStr	Classical mathematical models for description and prediction of experimental tumor growth.
title_full_unstemmed	Classical mathematical models for description and prediction of experimental tumor growth.
title_sort	classical mathematical models for description and prediction of experimental tumor growth.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/19e31e19343449d79eb789570472c361
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Classical mathematical models for description and prediction of experimental tumor growth.

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