Francesco G Cordoni, Marta Missiaggia, Chiara La Tessa, Emanuele Scifoni
{"title":"在不同的辐射生物物理模型中考虑了多重水平的随机性:从物理学到生物学。","authors":"Francesco G Cordoni, Marta Missiaggia, Chiara La Tessa, Emanuele Scifoni","doi":"10.1080/09553002.2023.2146230","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>In the present paper we investigate how some stochastic effects are included in a class of radiobiological models with particular emphasis on how such randomnesses reflect into the predicted cell survival curve.</p><p><strong>Materials and methods: </strong>We consider four different models, namely the <i>Generalized Stochastic Microdosimetric Model</i> GSM<sup>2</sup>, in its original full form, the <i>Dirac</i> GSM<sup>2</sup> the <i>Poisson</i> GSM<sup>2</sup><math></math> and the <i>Repair-Misrepair Model</i> (RMR). While GSM<sup>2</sup><math></math> and the RMR models are known in literature, the Dirac and the Poisson GSM<sup>2</sup><math><mi> </mi></math> have been newly introduced in this work. We further numerically investigate via Monte Carlo simulation of four different particle beams, how the proposed stochastic approximations reflect into the predicted survival curves. To achieve these results, we consider different ion species at energies of interest for therapeutic applications, also including a mixed field scenario.</p><p><strong>Results: </strong>We show how the <i>Dirac</i> GSM<sup>2</sup><math><mo>,</mo></math> the <i>Poisson</i> GSM<sup>2</sup><math></math> and the RMR can be obtained from the GSM<sup>2</sup><math></math> under suitable approximations on the stochasticity considered. We analytically derive the cell survival curve predicted by the four models, characterizing rigorously the high and low dose limits. We further study how the theoretical findings emerge also using Monte Carlo numerical simulations.</p><p><strong>Conclusions: </strong>We show how different models include different levels of stochasticity in the description of cellular response to radiation. This translates into different cell survival predictions depending on the radiation quality.</p>","PeriodicalId":14261,"journal":{"name":"International Journal of Radiation Biology","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Multiple levels of stochasticity accounted for in different radiation biophysical models: from physics to biology.\",\"authors\":\"Francesco G Cordoni, Marta Missiaggia, Chiara La Tessa, Emanuele Scifoni\",\"doi\":\"10.1080/09553002.2023.2146230\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>In the present paper we investigate how some stochastic effects are included in a class of radiobiological models with particular emphasis on how such randomnesses reflect into the predicted cell survival curve.</p><p><strong>Materials and methods: </strong>We consider four different models, namely the <i>Generalized Stochastic Microdosimetric Model</i> GSM<sup>2</sup>, in its original full form, the <i>Dirac</i> GSM<sup>2</sup> the <i>Poisson</i> GSM<sup>2</sup><math></math> and the <i>Repair-Misrepair Model</i> (RMR). While GSM<sup>2</sup><math></math> and the RMR models are known in literature, the Dirac and the Poisson GSM<sup>2</sup><math><mi> </mi></math> have been newly introduced in this work. We further numerically investigate via Monte Carlo simulation of four different particle beams, how the proposed stochastic approximations reflect into the predicted survival curves. To achieve these results, we consider different ion species at energies of interest for therapeutic applications, also including a mixed field scenario.</p><p><strong>Results: </strong>We show how the <i>Dirac</i> GSM<sup>2</sup><math><mo>,</mo></math> the <i>Poisson</i> GSM<sup>2</sup><math></math> and the RMR can be obtained from the GSM<sup>2</sup><math></math> under suitable approximations on the stochasticity considered. We analytically derive the cell survival curve predicted by the four models, characterizing rigorously the high and low dose limits. We further study how the theoretical findings emerge also using Monte Carlo numerical simulations.</p><p><strong>Conclusions: </strong>We show how different models include different levels of stochasticity in the description of cellular response to radiation. This translates into different cell survival predictions depending on the radiation quality.</p>\",\"PeriodicalId\":14261,\"journal\":{\"name\":\"International Journal of Radiation Biology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Radiation Biology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1080/09553002.2023.2146230\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Radiation Biology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1080/09553002.2023.2146230","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOLOGY","Score":null,"Total":0}
Multiple levels of stochasticity accounted for in different radiation biophysical models: from physics to biology.
Purpose: In the present paper we investigate how some stochastic effects are included in a class of radiobiological models with particular emphasis on how such randomnesses reflect into the predicted cell survival curve.
Materials and methods: We consider four different models, namely the Generalized Stochastic Microdosimetric Model GSM2, in its original full form, the Dirac GSM2 the Poisson GSM2 and the Repair-Misrepair Model (RMR). While GSM2 and the RMR models are known in literature, the Dirac and the Poisson GSM2 have been newly introduced in this work. We further numerically investigate via Monte Carlo simulation of four different particle beams, how the proposed stochastic approximations reflect into the predicted survival curves. To achieve these results, we consider different ion species at energies of interest for therapeutic applications, also including a mixed field scenario.
Results: We show how the Dirac GSM2 the Poisson GSM2 and the RMR can be obtained from the GSM2 under suitable approximations on the stochasticity considered. We analytically derive the cell survival curve predicted by the four models, characterizing rigorously the high and low dose limits. We further study how the theoretical findings emerge also using Monte Carlo numerical simulations.
Conclusions: We show how different models include different levels of stochasticity in the description of cellular response to radiation. This translates into different cell survival predictions depending on the radiation quality.
期刊介绍:
The International Journal of Radiation Biology publishes original papers, reviews, current topic articles, technical notes/reports, and meeting reports on the effects of ionizing, UV and visible radiation, accelerated particles, electromagnetic fields, ultrasound, heat and related modalities. The focus is on the biological effects of such radiations: from radiation chemistry to the spectrum of responses of living organisms and underlying mechanisms, including genetic abnormalities, repair phenomena, cell death, dose modifying agents and tissue responses. Application of basic studies to medical uses of radiation extends the coverage to practical problems such as physical and chemical adjuvants which improve the effectiveness of radiation in cancer therapy. Assessment of the hazards of low doses of radiation is also considered.