Accuracy analysis of different dose calculation algorithms for locally advanced pancreatic cancer stereotactic body radiotherapy.
J Cancer
; 14(14): 2694-2699, 2023.
Article
em En
| MEDLINE
| ID: mdl-37779867
ABSTRACT
Background:
The dose distribution in different optimization algorithm plans of stereotactic radiotherapy (SBRT) for locally advanced pancreatic cancer (LAPC) were compared and analyzed using monte carlo dose calculate algorithm (MC).Methods:
A retrospective study analyzed 26 LAPC patients treated with SBRT. The SBRT plans were designed by raytracing (RT) and fine size pencil beam (FSPB) algorithms in the CyberKnife (CK) precision system, all of which met the requirements of clinical target dose and organ at risk (OAR). Keeping the original optimization parameters unchanged, the RT and FSPB algorithm plans were recalculated by MC algorithm. The accuracy of different algorithm plnas were compared and analyzed by using planning parameters and dose distribution.Results:
There was no significant differences in the coverage and conformal index (CI) of the planned target volume (PTV) between RT and FSPB algorithm plans, but dose distribution of organ at risk (OAR) and the maximum dose outside the PTV boundary of 2 cm (D2cm) were lower in FSPB plans compared to RT plans, and this difference was statistically significant with p-values < 0.05. Compared to the MC algorithm, both RT algorithm and FSPB algorithm overestimated dose of the PTV and OAR. The RT algorithm was more consistent with the MC algorithm than the FSPB algorithm. The relative error of PTV coverage within the RT algorithm was 8.02% ± 1.53%, and the relative error range of OAR dose parameters was 3.32% -12.73%.Conclusion:
Although the FSPB algorithm could achieve rapid dose drop-off around the PTV and lower dose distribution in the OAR for pancreatic cancer SBRT plans, the algorithm error were higher than the RT algorithm. RT and FSPB algorithm overestimated the dose in the target and OAR. That was important to evaluate the clinical plans.
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Base de dados:
MEDLINE
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article