Chaikh Abdulhamid, Balosso Jacques
Department of Radiation Oncology and Medical Physics, University Hospital of Grenoble (CHU-GA), Grenoble, France; ; France HADRON National Research Infrastructure, IPNL, Lyon, France.
Department of Radiation Oncology and Medical Physics, University Hospital of Grenoble (CHU-GA), Grenoble, France; ; France HADRON National Research Infrastructure, IPNL, Lyon, France; ; Department of Radiation Oncology and Medical Physics, University Grenoble, Alpes, Grenoble, France.
Transl Lung Cancer Res. 2016 Dec;5(6):681-687. doi: 10.21037/tlcr.2016.11.03.
To apply the statistical bootstrap analysis and dosimetric criteria's to assess the change of prescribed dose (PD) for lung cancer to maintain the same clinical results when using new generations of dose calculation algorithms.
Nine lung cancer cases were studied. For each patient, three treatment plans were generated using exactly the same beams arrangements. In plan 1, the dose was calculated using pencil beam convolution (PBC) algorithm turning on heterogeneity correction with modified batho (PBC-MB). In plan 2, the dose was calculated using anisotropic analytical algorithm (AAA) and the same PD, as plan 1. In plan 3, the dose was calculated using AAA with monitor units (MUs) obtained from PBC-MB, as input. The dosimetric criteria's include MUs, delivered dose at isocentre (Diso) and calculated dose to 95% of the target volume (D95). The bootstrap method was used to assess the significance of the dose differences and to accurately estimate the 95% confidence interval (95% CI). Wilcoxon and Spearman's rank tests were used to calculate P values and the correlation coefficient (ρ).
Statistically significant for dose difference was found using point kernel model. A good correlation was observed between both algorithms types, with ρ>0.9. Using AAA instead of PBC-MB, an adjustment of the PD in the isocentre is suggested.
For a given set of patients, we assessed the need to readjust the PD for lung cancer using dosimetric indices and bootstrap statistical method. Thus, if the goal is to keep on with the same clinical results, the PD for lung tumors has to be adjusted with AAA. According to our simulation we suggest to readjust the PD by 5% and an optimization for beam arrangements to better protect the organs at risks (OARs).
应用统计自助法分析和剂量学标准，评估使用新一代剂量计算算法时，为维持相同临床结果肺癌处方剂量（PD）的变化。
研究了9例肺癌病例。对于每位患者，使用完全相同的射束排列生成三个治疗计划。在计划1中，使用笔形束卷积（PBC）算法并开启采用改良巴托（PBC-MB）的不均匀性校正来计算剂量。在计划2中，使用各向异性分析算法（AAA）并采用与计划1相同的PD来计算剂量。在计划3中，使用AAA并将从PBC-MB获得的监测单位（MU）作为输入来计算剂量。剂量学标准包括MU、等中心处的交付剂量（Diso）以及计算得到的靶体积95%的剂量（D95）。采用自助法评估剂量差异的显著性并准确估计95%置信区间（95%CI）。使用Wilcoxon和Spearman秩检验来计算P值和相关系数（ρ）。
使用点核模型发现剂量差异具有统计学显著性。观察到两种算法类型之间具有良好的相关性，ρ>0.9。建议使用AAA代替PBC-MB时，对等中心处的PD进行调整。
对于给定的一组患者，我们使用剂量学指标和自助统计方法评估了调整肺癌PD的必要性。因此，如果目标是保持相同的临床结果，肺癌肿瘤的PD必须使用AAA进行调整。根据我们的模拟，建议将PD重新调整5%并优化射束排列以更好地保护危及器官（OAR）。
