Study on the ability of the three-dose-volume-histogram-gamma (3DVH-γ) analysis and bio-mathematical model in detecting dose changes caused by dose-calculation-grid-size (DCGS)
Research Square Platform LLC, 2020
Online
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Zugriff:
Objective : To explore the efficacy and sensitivity of 3DVH-γanalysis and bio-mathematical model for cervical cancer in detecting dose changes caused by dose-calculation-grid-size(DCGS). Methods: 17 patients’ plans for cervical cancer were enrolled(Pinnacle TPS,VMAT), and the DCGS was changed from 2.0mm to 5.0mm to calculate the planned dose respectively. The dose distribution calculated by DCGS = 2.0mm as the “ reference ” data set (RDS) , the dose distribution calculated by the rest DCGS as the“measurement”data set (MDS), the 3DVH-γ passing rates and the (N)TCPs of the all structures under different DCGS were obtained , and then analyze the ability of 3DVH-γ analysis and (N)TCP model in detecting dose changes and what factors affect this ability. Results: The effect of DCGS on planned dose was obvious. When the γ-standard was 1.0mm, 1.0% and 10.0%, the difference of the results of the DCGS on dose-effect could be detected by 3DVH-γ analysis ( p s0.05 accounted for the majority. With DCGS=2.0mm being RDS, ∆γ-passing-rate presented the same trend with ∆(N)TCPs of all structures except for the femurs only when the 1.0mm, 1.0% and 10.0% standards were adopted for the 3DVH-γ analysis. Conclusions: The 3DVH-γ analysis and bio-mathematical model can be used to analyze the effect of DCGS on the planned dose. For comparison, the former’s detection ability has a lot to do with the designed standard, and the latter’s capability is related to the parameters and calculated accuracy instrinsically.
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Study on the ability of the three-dose-volume-histogram-gamma (3DVH-γ) analysis and bio-mathematical model in detecting dose changes caused by dose-calculation-grid-size (DCGS)
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Autor/in / Beteiligte Person: | Zhu, Sijin ; Bai, Han ; Wu, Xingrao ; Yan, Jiawen ; Liu, Xuhong ; Chen, Feihu |
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Veröffentlichung: | Research Square Platform LLC, 2020 |
Medientyp: | unknown |
DOI: | 10.21203/rs.3.rs-17543/v2 |
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