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A predictive model for personalization of nanotechnology-based phototherapy in cancer treatment

A major challenge in radiation oncology is the prediction and optimization of clinical responses in a personalized manner. Recently, nanotechnology-based cancer treatments are being combined with photodynamic therapy (PDT) and photothermal therapy (PTT). Predictive models based on machine learning t...

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Autores principales: Varon, Eli, Blumrosen, Gaddi, Shefi, Orit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9999042/
https://www.ncbi.nlm.nih.gov/pubmed/36911792
http://dx.doi.org/10.3389/fonc.2022.1037419
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author Varon, Eli
Blumrosen, Gaddi
Shefi, Orit
author_facet Varon, Eli
Blumrosen, Gaddi
Shefi, Orit
author_sort Varon, Eli
collection PubMed
description A major challenge in radiation oncology is the prediction and optimization of clinical responses in a personalized manner. Recently, nanotechnology-based cancer treatments are being combined with photodynamic therapy (PDT) and photothermal therapy (PTT). Predictive models based on machine learning techniques can be used to optimize the clinical setup configuration, including such parameters as laser radiation intensity, treatment duration, and nanoparticle features. In this article we demonstrate a methodology that can be used to identify the optimal treatment parameters for PDT and PTT by collecting data from in vitro cytotoxicity assay of PDT/PTT-induced cell death using a single nanocomplex. We construct three machine learning prediction models, employing regression, interpolation, and low- degree analytical function fitting, to predict the laser radiation intensity and duration settings that maximize the treatment efficiency. To examine the accuracy of these prediction models, we construct a dedicated dataset for PDT, PTT, and a combined treatment; this dataset is based on cell death measurements after light radiation treatment and is divided into training and test sets. The preliminary results show that the performance of all three models is sufficient, with death rate errors of 0.09, 0.15, and 0.12 for the regression, interpolation, and analytical function fitting approaches, respectively. Nevertheless, due to its simple form, the analytical function method has an advantage in clinical application and can be used for further analysis of the sensitivity of performance to the treatment parameters. Overall, the results of this study form a baseline for a future personalized prediction model based on machine learning in the domain of combined nanotechnology- and phototherapy-based cancer treatment.
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spelling pubmed-99990422023-03-11 A predictive model for personalization of nanotechnology-based phototherapy in cancer treatment Varon, Eli Blumrosen, Gaddi Shefi, Orit Front Oncol Oncology A major challenge in radiation oncology is the prediction and optimization of clinical responses in a personalized manner. Recently, nanotechnology-based cancer treatments are being combined with photodynamic therapy (PDT) and photothermal therapy (PTT). Predictive models based on machine learning techniques can be used to optimize the clinical setup configuration, including such parameters as laser radiation intensity, treatment duration, and nanoparticle features. In this article we demonstrate a methodology that can be used to identify the optimal treatment parameters for PDT and PTT by collecting data from in vitro cytotoxicity assay of PDT/PTT-induced cell death using a single nanocomplex. We construct three machine learning prediction models, employing regression, interpolation, and low- degree analytical function fitting, to predict the laser radiation intensity and duration settings that maximize the treatment efficiency. To examine the accuracy of these prediction models, we construct a dedicated dataset for PDT, PTT, and a combined treatment; this dataset is based on cell death measurements after light radiation treatment and is divided into training and test sets. The preliminary results show that the performance of all three models is sufficient, with death rate errors of 0.09, 0.15, and 0.12 for the regression, interpolation, and analytical function fitting approaches, respectively. Nevertheless, due to its simple form, the analytical function method has an advantage in clinical application and can be used for further analysis of the sensitivity of performance to the treatment parameters. Overall, the results of this study form a baseline for a future personalized prediction model based on machine learning in the domain of combined nanotechnology- and phototherapy-based cancer treatment. Frontiers Media S.A. 2023-01-04 /pmc/articles/PMC9999042/ /pubmed/36911792 http://dx.doi.org/10.3389/fonc.2022.1037419 Text en Copyright © 2023 Varon, Blumrosen and Shefi https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Varon, Eli
Blumrosen, Gaddi
Shefi, Orit
A predictive model for personalization of nanotechnology-based phototherapy in cancer treatment
title A predictive model for personalization of nanotechnology-based phototherapy in cancer treatment
title_full A predictive model for personalization of nanotechnology-based phototherapy in cancer treatment
title_fullStr A predictive model for personalization of nanotechnology-based phototherapy in cancer treatment
title_full_unstemmed A predictive model for personalization of nanotechnology-based phototherapy in cancer treatment
title_short A predictive model for personalization of nanotechnology-based phototherapy in cancer treatment
title_sort predictive model for personalization of nanotechnology-based phototherapy in cancer treatment
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9999042/
https://www.ncbi.nlm.nih.gov/pubmed/36911792
http://dx.doi.org/10.3389/fonc.2022.1037419
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