Evolution of physico-mathematical models in radiobiology and their application in ionizing radiation therapies

Garzón, Orozco; Plazas, María Cristina; Salazar, Edison de Jesus

doi:10.18180/tecciencia.2014.17.2

Cited by 3 publications

(2 citation statements)

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“…For instance, atomic-level effects of free radicals induced by ionizing radiation through to clinical scoring of late radiotherapy responses can span

, for which the entirety of events are unlikely to adequately captured by any one single approach. 114 Radiotherapy is also data intensive in that it encompasses functional imaging data and requires anatomical information, which, when coupled with biological markers from tumor and peripheral bloods, lends itself to the use of informatics approaches that can cope with high-dimensionality and heterogeneity. 115 …”

Section: Modeling Strategiesmentioning

confidence: 99%

Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges

2021

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The power of predictive modeling for radiotherapy outcomes has historically been limited by an inability to adequately capture patient-specific variabilities; however, nextgeneration platforms together with imaging technologies and powerful bioinformatic tools have facilitated strategies and provided optimism. Integrating clinical, biological, imaging, and treatment-specific data for more accurate prediction of tumor control probabilities or risk of radiation-induced side effects are high-dimensional problems whose solutions could have widespread benefits to a diverse patient population-we discuss technical approaches toward this objective. Increasing interest in the above is specifically reflected by the emergence of two nascent fields, which are distinct but complementary: radiogenomics, which broadly seeks to integrate biological risk factors together with treatment and diagnostic information to generate individualized patient risk profiles, and radiomics, which further leverages large-scale imaging correlates and extracted features for the same purpose. We review classical analytical and datadriven approaches for outcomes prediction that serve as antecedents to both radiomic and radiogenomic strategies. Discussion then focuses on uses of conventional and deep machine learning in radiomics. We further consider promising strategies for the harmonization of highdimensional, heterogeneous multiomics datasets (panomics) and techniques for nonparametric validation of best-fit models. Strategies to overcome common pitfalls that are unique to dataintensive radiomics are also discussed. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…For instance, atomic-level effects of free radicals induced by ionizing radiation through to clinical scoring of late radiotherapy responses can span

Section: Modeling Strategiesmentioning

confidence: 99%

Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges

2021

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“…У нормальних клітинах після впливу малих доз виявлено стимуляцію ендогенної антиоксидантної системи репарації, а на рівні організму відповідь на дію малих доз радіації супроводжується і активацією імунної системи. Показано, що низькі дози радіації можуть стимулювати імунологічні реакції шляхом полегшення процесу передачі сигналу [23,[28][29][30][31][32][33][34].…”

Section: Eejp 2 (2021)unclassified

Modeling of Molecular Mechanisms of Radiation Adaptive Response Formation

2021

EEJP

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The phenomenon of adaptive response is expressed in the increase of resistance of a biological object to high doses of mutagens under the conditions of previous exposure to these (or other) mutagens in low doses. Low doses of mutagen activate a number of protective mechanisms in a living object, which are called hormetic. Thus, the adaptive response and hormesis are links in the same chain. Radiation hormesis refers to the generally positive effect of low doses of low LET radiation on biological objects. The phenomenology of radiation-induced adaptive response and radiation hormesis for biological objects of different levels of organization is considered; the review of existing theories describing the dose-effect relationship has been reviewed. The hypothesis proposing one of the mechanisms of formation of radiation adaptive response of cells taking into account the conformational structure of chromatin has been submitted. The analysis of modern concepts of the phenomenon of hormesis on the basis of modeling of molecular mechanisms of formation of hormetic reactions to low-dose low LET radiation has been carried out. The parameters that can be used for quantitative and graphical evaluation of the phenomenon of hormesis was considered, and a formula for calculating the coefficient of radiation-induced adaptive response has been proposed. A review of mathematical models describing the radiation relative risk of gene mutations and neoplastic transformations at low-dose irradiation of cohorts has been performed. The following conclusions have been made: radiation hormesis and adaptive response are generally recognized as real and reproducible biological phenomena, which should be considered as very important phenomena of evolutionarily formed biological protection of living organisms from ionizing radiation. The hormesis model of dose-response relationship makes much more accurate predictions of a living object's response to radiation (or other stressors) in the low-dose range than the linear threshold (LNT) model does. The LNT model can adequately describe reactions only in the region of high doses of radiation, and, therefore, extrapolation modeling of biological object’s reactions from the zone of high doses to low doses is not correct.

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2017

Radiation Biology for Medical Physicists

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Evolution of physico-mathematical models in radiobiology and their application in ionizing radiation therapies

Cited by 3 publications

References 9 publications

Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges

Radiomic and radiogenomic modeling for radiotherapy: strategies, pitfalls, and challenges

Modeling of Molecular Mechanisms of Radiation Adaptive Response Formation

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