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Practical Understanding of Cancer Model Identifiability in Clinical Applications

Mathematical models are a core component in the foundation of cancer theory and have been developed as clinical tools in precision medicine. Modeling studies for clinical applications often assume an individual’s characteristics can be represented as parameters in a model and are used to explain, pr...

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Autores principales: Phan, Tin, Bennett, Justin, Patten, Taylor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9961656/
https://www.ncbi.nlm.nih.gov/pubmed/36836767
http://dx.doi.org/10.3390/life13020410
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author Phan, Tin
Bennett, Justin
Patten, Taylor
author_facet Phan, Tin
Bennett, Justin
Patten, Taylor
author_sort Phan, Tin
collection PubMed
description Mathematical models are a core component in the foundation of cancer theory and have been developed as clinical tools in precision medicine. Modeling studies for clinical applications often assume an individual’s characteristics can be represented as parameters in a model and are used to explain, predict, and optimize treatment outcomes. However, this approach relies on the identifiability of the underlying mathematical models. In this study, we build on the framework of an observing-system simulation experiment to study the identifiability of several models of cancer growth, focusing on the prognostic parameters of each model. Our results demonstrate that the frequency of data collection, the types of data, such as cancer proxy, and the accuracy of measurements all play crucial roles in determining the identifiability of the model. We also found that highly accurate data can allow for reasonably accurate estimates of some parameters, which may be the key to achieving model identifiability in practice. As more complex models required more data for identification, our results support the idea of using models with a clear mechanism that tracks disease progression in clinical settings. For such a model, the subset of model parameters associated with disease progression naturally minimizes the required data for model identifiability.
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spelling pubmed-99616562023-02-26 Practical Understanding of Cancer Model Identifiability in Clinical Applications Phan, Tin Bennett, Justin Patten, Taylor Life (Basel) Article Mathematical models are a core component in the foundation of cancer theory and have been developed as clinical tools in precision medicine. Modeling studies for clinical applications often assume an individual’s characteristics can be represented as parameters in a model and are used to explain, predict, and optimize treatment outcomes. However, this approach relies on the identifiability of the underlying mathematical models. In this study, we build on the framework of an observing-system simulation experiment to study the identifiability of several models of cancer growth, focusing on the prognostic parameters of each model. Our results demonstrate that the frequency of data collection, the types of data, such as cancer proxy, and the accuracy of measurements all play crucial roles in determining the identifiability of the model. We also found that highly accurate data can allow for reasonably accurate estimates of some parameters, which may be the key to achieving model identifiability in practice. As more complex models required more data for identification, our results support the idea of using models with a clear mechanism that tracks disease progression in clinical settings. For such a model, the subset of model parameters associated with disease progression naturally minimizes the required data for model identifiability. MDPI 2023-02-01 /pmc/articles/PMC9961656/ /pubmed/36836767 http://dx.doi.org/10.3390/life13020410 Text en © 2023 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 Article
Phan, Tin
Bennett, Justin
Patten, Taylor
Practical Understanding of Cancer Model Identifiability in Clinical Applications
title Practical Understanding of Cancer Model Identifiability in Clinical Applications
title_full Practical Understanding of Cancer Model Identifiability in Clinical Applications
title_fullStr Practical Understanding of Cancer Model Identifiability in Clinical Applications
title_full_unstemmed Practical Understanding of Cancer Model Identifiability in Clinical Applications
title_short Practical Understanding of Cancer Model Identifiability in Clinical Applications
title_sort practical understanding of cancer model identifiability in clinical applications
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9961656/
https://www.ncbi.nlm.nih.gov/pubmed/36836767
http://dx.doi.org/10.3390/life13020410
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