Dosimetric assessment of conventional and advanced algorithms in clinical stereotactic radiotherapy

临床立体定向放射治疗中传统算法和先进算法的剂量学评估

阅读:1

Abstract

PURPOSE: To systematically compare the dosimetric performance of conventional (Ray Tracing, AAA) and advanced (Monte Carlo, Acuros XB) dose calculation algorithms across homogeneous and heterogeneous tissues in stereotactic radiotherapy (SRT) and stereotactic body radiotherapy (SBRT). METHODS: A retrospective analysis of 125 SRT cases (brain: 50, lung: 20, liver: 20, spine: 35) was conducted using CyberKnife and Varian systems. Plans were originally created using Type B (Anisotropic Analytical Algorithm [AAA] and Ray Tracing) algorithms and were subsequently recalculated using Type C (Acuros XB and Monte Carlo) algorithms, while maintaining identical beam geometry and monitor units. Dosimetric parameters (D(95%), D(mean), D(max), CI, HI, GI) were evaluated. Validation included point dose measurements with Cheese Phantom and gamma index analysis using the PTW 1600 SRS Phantom. RESULTS: In lung cases, Type B algorithms overestimated D(95%) by 14% compared to Monte Carlo, which reduced D(mean) by 13.7% and CI by 25.8%. In liver, Acuros XB lowered D(mean) by 21.4% with a 0.8% CI reduction. For spine, Monte Carlo reduced D(95%) by 3.4%, with a 1.1% drop in D(mean) and stable CI. Brain cases showed minimal differences, with Monte Carlo increasing CI by 2.5% (1.19 vs. 1.16). Gamma pass rates exceeded 98% for Monte Carlo and Acuros XB, surpassing Ray Tracing and AAA (≤96%). CONCLUSION: Advanced algorithms demonstrated superior dose accuracy, homogeneity, and organs at risk (OAR) sparing in heterogeneous anatomical regions. Despite higher computational requirements, their clinical implementation is justified for SRT/SBRT planning. This study supports a site-specific approach, advocating for advanced algorithm use in anatomically complex scenarios.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。