Valdes G, Scheuermann R, Hung C Y, Olszanski A, Bellerive M, Solberg T D
Radiation Oncology Department, Perelman Center for Advanced Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19123.
Med Phys. 2016 Jul;43(7):4323. doi: 10.1118/1.4953835.
It is common practice to perform patient-specific pretreatment verifications to the clinical delivery of IMRT. This process can be time-consuming and not altogether instructive due to the myriad sources that may produce a failing result. The purpose of this study was to develop an algorithm capable of predicting IMRT QA passing rates a priori.
From all treatment, 498 IMRT plans sites were planned in eclipse version 11 and delivered using a dynamic sliding window technique on Clinac iX or TrueBeam Linacs. 3%/3 mm local dose/distance-to-agreement (DTA) was recorded using a commercial 2D diode array. Each plan was characterized by 78 metrics that describe different aspects of their complexity that could lead to disagreements between the calculated and measured dose. A Poisson regression with Lasso regularization was trained to learn the relation between the plan characteristics and each passing rate.
Passing rates 3%/3 mm local dose/DTA can be predicted with an error smaller than 3% for all plans analyzed. The most important metrics to describe the passing rates were determined to be the MU factor (MU per Gy), small aperture score, irregularity factor, and fraction of the plan delivered at the corners of a 40 × 40 cm field. The higher the value of these metrics, the worse the passing rates.
The Virtual QA process predicts IMRT passing rates with a high likelihood, allows the detection of failures due to setup errors, and it is sensitive enough to detect small differences between matched Linacs.
对调强放射治疗（IMRT）进行针对患者的治疗前验证是常见做法。由于可能导致验证结果不通过的来源众多，这个过程可能耗时且并非完全具有指导意义。本研究的目的是开发一种能够预先预测IMRT质量保证（QA）通过率的算法。
在Eclipse 11版本中规划了498个IMRT计划部位，并使用Clinac iX或TrueBeam直线加速器上的动态滑动窗口技术进行治疗。使用商用二维二极管阵列记录3%/3毫米的局部剂量/距离一致性（DTA）。每个计划由78个指标表征，这些指标描述了其复杂性的不同方面，这些方面可能导致计算剂量与测量剂量之间出现差异。训练了带有套索正则化的泊松回归模型，以了解计划特征与每个通过率之间的关系。
对于所有分析的计划，3%/3毫米局部剂量/DTA的通过率预测误差小于3%。确定描述通过率的最重要指标为监测单位（MU）因子（每Gy的MU数）、小孔径分数、不规则因子以及在40×40厘米射野角落处照射的计划部分占比。这些指标的值越高，通过率越差。
虚拟QA过程能够高度准确地预测IMRT通过率，可检测由于摆位误差导致的失败情况，并且灵敏度足以检测匹配直线加速器之间的微小差异。
