A nomogram to predict radiation pneumonitis, derived from a combined analysis of rtog 9311 and institutional data
A nomogram to predict radiation pneumonitis, derived from a combined analysis of rtog 9311 and institutional data
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DOI：
10.1016/j.ijrobp.2007.04.077
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发表时间：
2007-11-15
影响因子：
7
通讯作者：
Deasy, Joseph O.
中科院分区：
文献类型：
--
作者：
Bradley, Jeffrey D.;Hope, Andrew;Deasy, Joseph O.
目的：为了测试华盛顿大学（WU）患者数据集，对其进行的分析表明，肿瘤位置、最大剂量和D35根据放射治疗肿瘤学组（RTOG）试验9311的患者数据库，最低剂量（最热35%肺体积）对预测放射性肺炎（PP）有价值。方法和材料：整个数据集包括324例接受确定性适形放射治疗的非小细胞肺癌患者（WU = 219，RTOG 9311 = 129）。对临床、剂量测定和肿瘤位置参数进行建模，以预测个体数据集和组合数据集中的RP。使用斯皮尔曼的秩相关（r）的单变量分析和多变量分析与RP的关联质量进行了评估;使用Wilcoxon秩和检验进行了亚组之间的比较。结果：WU模型预测RP表现不佳的RTOG 9311数据。RTOG 9311数据集中最具预测性的模型是单参数模型D15（r = 0.28）。结合数据集，最佳衍生模型是由平均肺剂量和上下大体肿瘤体积位置组成的双参数模型（r = 0.303）。一个方程和诺模图来预测RP的概率，得出使用合并patient population.Conclusions：统计模型来自一个大的池的候选模型，导致每个子集（WU或RTOG 9311），这并没有表现得很好，当应用到其他数据集的模型调整。然而，当数据被组合时，生成的模型在每个数据子集上都表现良好。最终的模型包括两个影响：由于下肺照射的风险更大，增加正常肺平均剂量的风险更大。这个公式和列线图可以帮助临床医生在肺癌放射治疗计划。(c)2007爱思唯尔公司
Purpose: To test the Washington University (WU) patient dataset, analysis of which suggested that superior-to-inferior tumor position, maximum dose, and D35 (minimum dose to the hottest 35% of the lung volume) were valuable to predict radiation pneumonitis (PP), against the patient database from Radiation Therapy Oncology Group (RTOG) trial 9311.Methods and Materials: The entire dataset consisted of 324 patients receiving definitive conformal radiotherapy for non-small-cell lung cancer (WU = 219, RTOG 9311 = 129). Clinical, dosimetric, and tumor location parameters were modeled to predict RP in the individual datasets and in a combined dataset. Association quality with RP was assessed using Spearman's rank correlation (r) for univariate analysis and multivariate analysis; comparison between subgroups was tested using the Wilcoxon rank sum test.Results: The WU model to predict RP performed poorly for the RTOG 9311 data. The most predictive model in the RTOG 9311 dataset was a single-parameter model, D15 (r = 0.28). Combining the datasets, the best derived model was a two-parameter model consisting of mean lung dose and superior-to-inferior gross tumor volume position (r = 0.303). An equation and nomogram to predict the probability of RP was derived using the combined patient population.Conclusions: Statistical models derived from a large pool of candidate models resulted in well-tuned models for each subset (WU or RTOG 9311), which did not perform well when applied to the other dataset. However, when the data were combined, a model was generated that performed well on each data subset. The final model incorporates two effects: greater risk due to inferior lung irradiation, and greater risk for increasing normal lung mean dose. This formula and nomogram may aid clinicians during radiation treatment planning for lung cancer. (c) 2007 Elsevier Inc.