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Protocol for mathematical prediction of patient response and survival to immune checkpoint inhibitor immunotherapy

This protocol describes the application of a mechanistic mathematical model of immune checkpoint inhibitor (ICI) immunotherapy to patient tumor imaging data for predicting solid tumor response and patient survival under ICI intervention. We describe steps for data collection and processing, data pip...

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Autores principales: Butner, Joseph D., Farhat, Maguy, Cristini, Vittorio, Chung, Caroline, Wang, Zhihui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9719106/
https://www.ncbi.nlm.nih.gov/pubmed/36595890
http://dx.doi.org/10.1016/j.xpro.2022.101886
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author Butner, Joseph D.
Farhat, Maguy
Cristini, Vittorio
Chung, Caroline
Wang, Zhihui
author_facet Butner, Joseph D.
Farhat, Maguy
Cristini, Vittorio
Chung, Caroline
Wang, Zhihui
author_sort Butner, Joseph D.
collection PubMed
description This protocol describes the application of a mechanistic mathematical model of immune checkpoint inhibitor (ICI) immunotherapy to patient tumor imaging data for predicting solid tumor response and patient survival under ICI intervention. We describe steps for data collection and processing, data pipelines, and approaches to increase precision. The protocol is highly predictive as early as the first restaging after treatment start and can be used with standard-of-care imaging measures. For complete details on the use and execution of this protocol, please refer to Butner et al. (2020)(1) and Butner et al. (2021).(2)
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spelling pubmed-97191062022-12-04 Protocol for mathematical prediction of patient response and survival to immune checkpoint inhibitor immunotherapy Butner, Joseph D. Farhat, Maguy Cristini, Vittorio Chung, Caroline Wang, Zhihui STAR Protoc Protocol This protocol describes the application of a mechanistic mathematical model of immune checkpoint inhibitor (ICI) immunotherapy to patient tumor imaging data for predicting solid tumor response and patient survival under ICI intervention. We describe steps for data collection and processing, data pipelines, and approaches to increase precision. The protocol is highly predictive as early as the first restaging after treatment start and can be used with standard-of-care imaging measures. For complete details on the use and execution of this protocol, please refer to Butner et al. (2020)(1) and Butner et al. (2021).(2) Elsevier 2022-11-30 /pmc/articles/PMC9719106/ /pubmed/36595890 http://dx.doi.org/10.1016/j.xpro.2022.101886 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Protocol
Butner, Joseph D.
Farhat, Maguy
Cristini, Vittorio
Chung, Caroline
Wang, Zhihui
Protocol for mathematical prediction of patient response and survival to immune checkpoint inhibitor immunotherapy
title Protocol for mathematical prediction of patient response and survival to immune checkpoint inhibitor immunotherapy
title_full Protocol for mathematical prediction of patient response and survival to immune checkpoint inhibitor immunotherapy
title_fullStr Protocol for mathematical prediction of patient response and survival to immune checkpoint inhibitor immunotherapy
title_full_unstemmed Protocol for mathematical prediction of patient response and survival to immune checkpoint inhibitor immunotherapy
title_short Protocol for mathematical prediction of patient response and survival to immune checkpoint inhibitor immunotherapy
title_sort protocol for mathematical prediction of patient response and survival to immune checkpoint inhibitor immunotherapy
topic Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9719106/
https://www.ncbi.nlm.nih.gov/pubmed/36595890
http://dx.doi.org/10.1016/j.xpro.2022.101886
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