Duggar William N, He Rui, Bhandari Rahul, Kanakamedala Madhava, Morris Bart, Rey-Dios Roberto, Vijayakumar Srinivasan, Yang Claus Chunli
Department of Radiation Oncology, University of Mississippi Medical Center, Jackson, MS, USA.
Department of Neurosurgery, University of Mississippi Medical Center, Jackson, MS, USA.
J Radiosurg SBRT. 2020;6(4):303-310.
To compare the consistency of the agreement between the Convolution and TMR10 algorithms using a homogeneous phantom and to identify target characteristics that lead to large changes in target isodose coverage when the Convolution algorithm is used in GammaPlan as opposed to the TMR10 algorithm.
The IROC phantom end-to-end test was performed and RTDose for both the TMR10 and Convolution algorithm were submitted for comparison to the measurement. Treatment plans for 16 patients and 26 different targets were retrospectively re-calculated with the Convolution algorithm when originally planned with the TMR10 algorithm. Multivariate regression was used to find statistically significant predictors of loss in target prescription isodose coverage.
Both algorithms agreed well with the IROC TLD measurement (within 1 %) and slightly better agreement was seen in the film analysis for the Convolution algorithm. After multivariate regression, small target volumes, < 1cm from air cavity, and minimum dose to target were potential predictors of large percentage loss of prescription isodose coverage (p = 0.049, 0.026, and 0.002, respectively).
Convolution and TMR10 appear to be equivalent in homogeneous situations. Some target characteristics have been identified that might be indications for use of the Convolution algorithm in clinical practice.
使用均匀模体比较卷积算法和TMR10算法之间协议的一致性,并确定与TMR10算法相比,在伽马刀治疗计划系统(GammaPlan)中使用卷积算法时导致靶区等剂量覆盖发生较大变化的靶区特征。
进行国际放射肿瘤学研究组(IROC)模体端到端测试,并提交TMR10算法和卷积算法的剂量体积直方图(RTDose)以与测量值进行比较。对16例患者和26个不同靶区的治疗计划进行回顾性重新计算,最初计划使用TMR10算法时,现使用卷积算法。采用多变量回归分析来寻找靶区处方等剂量覆盖损失的统计学显著预测因素。
两种算法与IROC热释光剂量计(TLD)测量结果吻合良好(在1%以内),在卷积算法的胶片分析中观察到稍好的一致性。多变量回归分析后,小靶区体积、距气腔<1cm以及靶区最小剂量是处方等剂量覆盖百分比大幅损失的潜在预测因素(p值分别为0.049、0.026和0.002)。
在均匀情况下,卷积算法和TMR10算法似乎等效。已确定一些靶区特征,这些特征可能是在临床实践中使用卷积算法的指征。