Vladimir A Pan, Alessio Parisi, David Bolst, Jesse Williams, Taku Inaniwa, Michael Jackson, Verity Ahren, Anatoly B Rosenfeld, Linh T Tran
{"title":"Comparative study of a microdosimetric biological weighting function for RBE<sub>10</sub>modeling in particle therapy with a solid state SOI microdosimeter.","authors":"Vladimir A Pan, Alessio Parisi, David Bolst, Jesse Williams, Taku Inaniwa, Michael Jackson, Verity Ahren, Anatoly B Rosenfeld, Linh T Tran","doi":"10.1088/1361-6560/ad9f1c","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>The recently developed V79-RBE<sub>10</sub>biological weighting function (BWF) model is a simple and robust tool for a fast relative biological effectiveness (RBE) assessment for comparing different exposure conditions in particle therapy. In this study, the RBE<sub>10</sub>derived by this model (through the Particle and Heavy Ion Transport code System (PHITS) simulated d(y) spectra) is compared with values of RBE<sub>10</sub>using experimentally derived d(y) spectra from a silicon-on-insulator (SOI) microdosimeter. 
 
Approach: Experimentally measured d(y) spectra are used to calculate an RBE<sub>10</sub>value utilizing the V79-RBE<sub>10</sub>BWF model as well as the modified microdosimetric kinetic model (MKM) to produce an RBE<sub>10</sub>-vs-y<sub>D</sub>trend for a wide range of ions. In addition, a beamline specific PHITS simulation was conducted which replicated the exact experimental conditions that were used with the SOI microdosimeter at the Heavy Ion Medical Accelerator in Chiba (HIMAC) biological beamline with<sup>12</sup>C ions. 
 
Main Results: The RBE<sub>10</sub>-vs-y<sub>D</sub>trend for<sup>1</sup>H,<sup>4</sup>He,<sup>7</sup>Li,<sup>12</sup>C,<sup>14</sup>N,<sup>16</sup>O,<sup>20</sup>Ne,<sup>28</sup>Si,<sup>56</sup>Fe, and<sup>124</sup>Xe ions is examined with good agreement found between the SOI microdosimeter derived RBE<sub>10</sub>values with the V79-RBE<sub>10</sub>BWF model and MKM, as well as the PHITS simulations for<sup>1</sup>H,<sup>4</sup>He,<sup>7</sup>Li,<sup>12</sup>C,<sup>16</sup>O and<sup>56</sup>Fe ions while some discrepancies were seen for<sup>14</sup>N,<sup>20</sup>Ne,<sup>28</sup>Si ions. Deviations have been attributed to the difference in the derivation of the d(y) spectra based on the different methods utilized. Good agreement was found between y<sub>D</sub>values and an over estimation was observed for RBE<sub>10</sub>values for the beamline specific simulation of the<sup>12</sup>C ion beam. 
 
Significance: Overall, this study shows that the SOI microdosimeter is a valuable tool that can be utilized for quick and accurate experimental derivation of the d(y) spectra, which can then be convoluted with the weighting function of the V79-RBE<sub>10</sub>BWF model to derive RBE<sub>10</sub>. The SOI microdosimeter is able to derive experimental values of y<sub>D</sub>and RBE<sub>10</sub>for various ions in any irradiation condition utilizing other radiobiological models.</p>","PeriodicalId":20185,"journal":{"name":"Physics in medicine and biology","volume":" ","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2024-12-13","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/ad9f1c","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
Objective: The recently developed V79-RBE10biological weighting function (BWF) model is a simple and robust tool for a fast relative biological effectiveness (RBE) assessment for comparing different exposure conditions in particle therapy. In this study, the RBE10derived by this model (through the Particle and Heavy Ion Transport code System (PHITS) simulated d(y) spectra) is compared with values of RBE10using experimentally derived d(y) spectra from a silicon-on-insulator (SOI) microdosimeter.
Approach: Experimentally measured d(y) spectra are used to calculate an RBE10value utilizing the V79-RBE10BWF model as well as the modified microdosimetric kinetic model (MKM) to produce an RBE10-vs-yDtrend for a wide range of ions. In addition, a beamline specific PHITS simulation was conducted which replicated the exact experimental conditions that were used with the SOI microdosimeter at the Heavy Ion Medical Accelerator in Chiba (HIMAC) biological beamline with12C ions.
Main Results: The RBE10-vs-yDtrend for1H,4He,7Li,12C,14N,16O,20Ne,28Si,56Fe, and124Xe ions is examined with good agreement found between the SOI microdosimeter derived RBE10values with the V79-RBE10BWF model and MKM, as well as the PHITS simulations for1H,4He,7Li,12C,16O and56Fe ions while some discrepancies were seen for14N,20Ne,28Si ions. Deviations have been attributed to the difference in the derivation of the d(y) spectra based on the different methods utilized. Good agreement was found between yDvalues and an over estimation was observed for RBE10values for the beamline specific simulation of the12C ion beam.
Significance: Overall, this study shows that the SOI microdosimeter is a valuable tool that can be utilized for quick and accurate experimental derivation of the d(y) spectra, which can then be convoluted with the weighting function of the V79-RBE10BWF model to derive RBE10. The SOI microdosimeter is able to derive experimental values of yDand RBE10for various ions in any irradiation condition utilizing other radiobiological models.
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