Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York 10065, USA.
Int J Radiat Oncol Biol Phys. 2013 Jan 1;85(1):182-9. doi: 10.1016/j.ijrobp.2012.03.024. Epub 2012 May 5.
To demonstrate the use of generalized equivalent uniform dose (gEUD) atlas for data pooling in radiation pneumonitis (RP) modeling, to determine the dependence of RP on gEUD, to study the consistency between data sets, and to verify the increased statistical power of the combination.
Patients enrolled in prospective phase I/II dose escalation studies of radiation therapy of non-small cell lung cancer at Memorial Sloan-Kettering Cancer Center (MSKCC) (78 pts) and the Netherlands Cancer Institute (NKI) (86 pts) were included; 10 (13%) and 14 (17%) experienced RP requiring steroids (RPS) within 6 months after treatment. gEUD was calculated from dose-volume histograms. Atlases for each data set were created using 1-Gy steps from exact gEUDs and RPS data. The Lyman-Kutcher-Burman model was fit to the atlas and exact gEUD data. Heterogeneity and inconsistency statistics for the fitted parameters were computed. gEUD maps of the probability of RPS rate≥20% were plotted.
The 2 data sets were homogeneous and consistent. The best fit values of the volume effect parameter a were small, with upper 95% confidence limit around 1.0 in the joint data. The likelihood profiles around the best fit a values were flat in all cases, making determination of the best fit a weak. All confidence intervals (CIs) were narrower in the joint than in the individual data sets. The minimum P value for correlations of gEUD with RPS in the joint data was .002, compared with P=.01 and .05 for MSKCC and NKI data sets, respectively. gEUD maps showed that at small a, RPS risk increases with gEUD.
The atlas can be used to combine gEUD and RPS information from different institutions and model gEUD dependence of RPS. RPS has a large volume effect with the mean dose model barely included in the 95% CI. Data pooling increased statistical power.
展示广义等效均匀剂量（gEUD）图谱在放射性肺炎（RP）建模中的数据汇总中的应用，确定 RP 与 gEUD 的依赖性，研究数据集之间的一致性，并验证组合的统计功效增加。
纳入纪念斯隆-凯特琳癌症中心（MSKCC）（78 例）和荷兰癌症研究所（NKI）（86 例）前瞻性 I/II 期放疗非小细胞肺癌剂量递增研究的患者；10 例（13%）和 14 例（17%）在治疗后 6 个月内发生需要类固醇治疗的放射性肺炎（RPS）。从剂量-体积直方图计算 gEUD。使用精确 gEUD 和 RPS 数据的 1 Gy 步长为每个数据集创建图谱。将 Lyman-Kutcher-Burman 模型拟合到图谱和精确 gEUD 数据中。计算拟合参数的异质性和不一致性统计数据。绘制 RPS 发生率≥20%的概率 gEUD 图。
这 2 个数据集是同质和一致的。体积效应参数 a 的最佳拟合值较小，联合数据中 95%置信上限约为 1.0。在所有情况下，最佳拟合 a 值的似然分布都很平坦，使得确定最佳拟合 a 值变得很弱。所有置信区间（CI）在联合数据中都比在单独的数据集中更窄。在联合数据中，gEUD 与 RPS 相关性的最小 P 值为.002，而 MSKCC 和 NKI 数据集的 P 值分别为.01 和.05。gEUD 图表明，在小 a 时，RPS 风险随 gEUD 增加而增加。
该图谱可用于合并来自不同机构的 gEUD 和 RPS 信息，并建立 gEUD 与 RPS 的依赖性模型。RPS 的体积效应很大，平均剂量模型几乎不包含在 95%CI 中。数据汇总增加了统计功效。
