Validation of the generalized stochastic microdosimetric model (GSM2) over a broad range of LET and particle beam type: a unique model for accurate description of (therapy relevant) radiation qualities.
Giulio Bordieri, Marta Missiaggia, Giorgio Cartechini, Marco Battestini, Lawrence Bronk, Fada Guan, David R Grosshans, Priyamvada Rai, Emanuele Scifoni, Chiara La Tessa, Gianluca Lattanzi, Francesco G Cordoni
{"title":"Validation of the generalized stochastic microdosimetric model (GSM<sup>2</sup>) over a broad range of LET and particle beam type: a unique model for accurate description of (therapy relevant) radiation qualities.","authors":"Giulio Bordieri, Marta Missiaggia, Giorgio Cartechini, Marco Battestini, Lawrence Bronk, Fada Guan, David R Grosshans, Priyamvada Rai, Emanuele Scifoni, Chiara La Tessa, Gianluca Lattanzi, Francesco G Cordoni","doi":"10.1088/1361-6560/ad9dab","DOIUrl":null,"url":null,"abstract":"<p><p>The present work shows the first extensive validation of the<i>Generalized Stochastic Microdosimetric Model</i>(GSM<sup>2</sup>). This mechanistic and probabilistic model is trained and tested over cell survival experiments conducted with two cell lines (H460 and H1437), three different types of radiation (protons, helium, and carbon ions), spanning a very broad LET range from 1 keV/μm up to more than 300 keV/μm.

Currently, the existing mechanistic radiation biophysical models show some limitations in describing cell killing without the addition of ad hoc corrections, especially in the high-LET regime, where the overkill effect is observed.

The experimental irradiation conditions have been accurately reproduced with Monte Carlo simulations using the GEANT4-based TOPAS computational toolkit. We show the main and unique features of GSM<sup>2</sup>, i.e., how it can accurately predict the biological response by considering the full information on the stochasticity of radiation through the microdosimetric spectrum, which is supposed to be the best descriptor of radiation quality. 

Well-matching results for different biological endpoints with the natural presence of the overkill effect fully display the predictive power of GSM<sup>2</sup>.
This study shows the complete generality and flexibility of GSM<sup>2</sup>and its ability to successfully predict the cell survival probability from very different particle radiation fields. Consequently, we demonstrate the dependence of the relative biological effectiveness on the whole microdosimetric spectrum, which fully includes the stochasticity inherently given by radiation-matter interaction.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physics in medicine and biology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1088/1361-6560/ad9dab","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
The present work shows the first extensive validation of theGeneralized Stochastic Microdosimetric Model(GSM2). This mechanistic and probabilistic model is trained and tested over cell survival experiments conducted with two cell lines (H460 and H1437), three different types of radiation (protons, helium, and carbon ions), spanning a very broad LET range from 1 keV/μm up to more than 300 keV/μm.
Currently, the existing mechanistic radiation biophysical models show some limitations in describing cell killing without the addition of ad hoc corrections, especially in the high-LET regime, where the overkill effect is observed.
The experimental irradiation conditions have been accurately reproduced with Monte Carlo simulations using the GEANT4-based TOPAS computational toolkit. We show the main and unique features of GSM2, i.e., how it can accurately predict the biological response by considering the full information on the stochasticity of radiation through the microdosimetric spectrum, which is supposed to be the best descriptor of radiation quality.
Well-matching results for different biological endpoints with the natural presence of the overkill effect fully display the predictive power of GSM2.
This study shows the complete generality and flexibility of GSM2and its ability to successfully predict the cell survival probability from very different particle radiation fields. Consequently, we demonstrate the dependence of the relative biological effectiveness on the whole microdosimetric spectrum, which fully includes the stochasticity inherently given by radiation-matter interaction.
期刊介绍:
The development and application of theoretical, computational and experimental physics to medicine, physiology and biology. Topics covered are: therapy physics (including ionizing and non-ionizing radiation); biomedical imaging (e.g. x-ray, magnetic resonance, ultrasound, optical and nuclear imaging); image-guided interventions; image reconstruction and analysis (including kinetic modelling); artificial intelligence in biomedical physics and analysis; nanoparticles in imaging and therapy; radiobiology; radiation protection and patient dose monitoring; radiation dosimetry