目的:基于线性加速器的立体定向放射外科(SRS)已成为同时管理多个颅内目标的主要手段。治疗计划系统(TPS)的最新改进使使用动态适形弧(DCA)和单一治疗等中心治疗多个脑转移瘤成为可能。然而,由于接受至少12Gy(V12)的健康组织的体积与发生放射性坏死的可能性有关,在最小化V12的同时平衡目标覆盖范围是影响SRS计划质量的关键因素。当前TPS允许用户调整影响计划优化的各种参数。这项工作的目的是量化负切缘对V12的影响,以用于颅内SRS计划管理多个脑转移。
方法:使用BrainlabElementsv3.0TPS(Brainlab,慕尼黑,德国),我们计算了V10,V12,V15,监控单元,和在我们的ElektaVersaHD上治疗2-10个转移的十七个SRS计划的符合性指数(Elekta,斯德哥尔摩,瑞典)直线加速器。我们比较了以5%的增量使用70%-90%处方等剂量线(IDL)优化的计划。
结果:不考虑治疗的转移瘤数量,与90%IDL相比,在较低处方IDL下的优化降低了V10、V12和V15并增加了MU(p<0.01)。然而,比较70%和75%的IDL优化,组织保留差异不大。由于案例数据的显着传播,一致性指数在不同的IDL上没有显示出一致的趋势。
结论:对于我们治疗多达10个转移瘤的计划,在IDL低于80%的情况下,组织保留的收益递减,加上治疗MU和剂量测定热点的增加,使得在较低IDL下的优化效果较差。在我们的诊所里,在咨询了医生之后,确定在80%IDL下的优化实现了V12,处理MU的最佳平衡,和最大剂量。实施基于LINAC的SRS程序的诊所可以考虑使用类似的评估来制定自己的临床协议。
OBJECTIVE: Linear accelerator-based stereotactic radiosurgery (SRS) has become a mainstay for simultaneous management of multiple intracranial targets. Recent improvements in treatment planning systems (TPS) have enabled treatment of multiple brain metastases using dynamic conformal arcs (DCA) and a single treatment isocenter. However, as the volume of healthy tissue receiving at least 12 Gy (V12) is linked to the probability of developing radionecrosis, balancing target coverage while minimizing V12 is a critical factor affecting SRS plan quality. Current TPS allow users to adjust various parameters influencing plan optimization. The purpose of this work is to quantify the effect of negative margins on V12 for cranial SRS plans managing multiple brain metastases.
METHODS: Using the Brainlab Elements v3.0 TPS (Brainlab, Munich, Germany), we calculated V10, V12, V15, monitor units, and conformity index for seventeen SRS plans treating 2-10 metastases on our Elekta Versa HD (Elekta, Stockholm, Sweden) linear accelerator. We compared plans optimized using 70%-90% prescription isodose lines (IDL) in 5% increments.
RESULTS: Irrespective of the number of treated metastases, optimization at a lower prescription IDL reduced V10, V12, and V15 and increased MU compared to the 90% IDL (p < 0.01). However, comparing the 70% and 75% IDL optimizations, there was little difference in tissue sparing. The conformity index showed no consistent trends at different IDLs due to a significant spread in case data.
CONCLUSIONS: For our plans treating up to 10 metastases, diminishing returns for tissue sparing at IDLs below 80% paired with increasing treatment MU and dosimetric hot spot made optimization at lower IDLs less favorable. In our clinic, after consulting with a physician, it was determined that optimization at the 80% IDL achieved the best balance of V12, treatment MU, and maximum dose. Clinics implementing LINAC-based SRS programs may consider using similar evaluations to develop their own clinical protocols.