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J Appl Clin Med Phys ; 14(5): 68-78, 2013 Sep 06.
Artigo em Inglês | MEDLINE | ID: mdl-24036860

RESUMO

This study evaluated the extent of improvement in dose predication accuracy achieved by the Fast Monte Carlo algorithm (MC) compared to the Ray Tracing algorithm (RAT) in stereotactic body radiotherapy (SBRT) of non-small cell lung cancer (NSCLC), and how their differences were influenced by the tumor site and size. Thirty-three NSCLC patients treated with SBRT by CyberKnife in 2011 were recruited. They were divided into the central target group (n = 17) and peripheral target group (n = 16) according to the RTOG 0236 guidelines. Each group was further divided into the large and small target subgroups. After the computation of treatment plans using RAT, a MC plan was generated using the same patient data and treatment parameters. Apart from the target reference point dose measurements, various dose parameters for the planning target volume (PTV) and organs at risk (OARs) were assessed. In addition, the "Fractional Deviation" (FDev) was also calculated for comparison, which was defined as the ratio of the RAT and MC values. For peripheral lung cases, RAT produced significantly higher dose values in all the reference points than MC. The FDev of all reference point doses and dose parameters was greater in the small target than the large target subgroup. For central lung cases, there was no significant reference point and OAR dose differences between RAT and MC. When comparing between the small target and large target subgroups, the FDev values of all the dose parameters and reference point doses did not show significant difference. Despite the shorter computation time, RAT was inferior to MC, in which the target dose was usually overestimated. RAT would not be recommended for SBRT of peripheral lung tumors regardless of the target size. However, it could be considered for large central lung tumors because its performance was comparable to MC.


Assuntos
Algoritmos , Carcinoma Pulmonar de Células não Pequenas/cirurgia , Neoplasias Pulmonares/cirurgia , Método de Monte Carlo , Radiocirurgia , Planejamento da Radioterapia Assistida por Computador , Humanos , Órgãos em Risco , Dosagem Radioterapêutica , Estudos Retrospectivos , Carga Tumoral
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