Modeling the risk of radiation-induced acute esophagitis for combined Washington University and RTOG trial 93-11 lung cancer patients.
Modeling the risk of radiation-induced acute esophagitis for combined Washington University and RTOG trial 93-11 lung cancer patients.
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DOI：
10.1016/j.ijrobp.2011.02.052
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发表时间：
2012-04-01
影响因子：
7
通讯作者：
Deasy, Joseph O.
中科院分区：
文献类型：
--
作者：
Huang, Ellen X.;Bradley, Jeffrey D.;El Naqa, Issam;Hope, Andrew J.;Lindsay, Patricia E.;Bosch, Walter R.;Matthews, John W.;Sause, William T.;Graham, Mary V.;Deasy, Joseph O.
目的：建立非小细胞肺癌患者接受明确放射治疗(RT)后发生严重急性食管炎(AE)风险的最大预测模型。数据集包括华盛顿大学和RTOG 93-11临床试验数据(事件/患者：120/374，WUSTL=101/237，RTOG9311=19/137)。根据剂量学和临床参数(患者年龄、性别、体重减轻、化疗前、同期化疗、分割大小)建立统计模型。从归档的治疗计划中提取了广泛的剂量-体积参数，包括Dx、Vx、MOHx(最热x%体积的平均值)、MOCx(最冷x%体积的平均值)和gEUD(广义等效均匀剂量)值。预测急性食管炎(RTOG分级2级或更高)的最重要的单项参数是MOH85、平均食道剂量(MED)和V30。上下级加权剂量中心位置被推导出来，但没有发现显着。分数大小在单变量Logistic分析中有显著意义(Spearman R=0.421，p<；0.00001)，但在多变量Logistic模型中无显著意义。采用交叉验证建立模型，确定最优模型大小只需要两个参数(MOH85和同步化疗，在Bootstrap模型重建时稳健选择)。平均食道剂量(MED)是首选，而不是MOH85，因为它提供了几乎相同的统计性能，并且更容易计算。AE风险以(0.0688*MED+1.5*ConChemo-3.13)的逻辑函数给出，其中MED以伽玛射线为单位，如果同时给予化疗，则ConChemo为1(是)，或0(否)。该模型与观察到的声发射风险相关，斯皮尔曼系数为0.629(p<；0.000001)。通过交叉验证建立的多变量统计模型表明，在WUSTL和RTOG 93-11联合数据试验数据集中，基于平均剂量和同时使用化疗的双变量Logistic模型可以有力地预测急性食管炎的风险。
To construct a maximally predictive model of the risk of severe acute esophagitis (AE) for patients who receive definitive radiation therapy (RT) for non–small-cell lung cancer. The dataset includes Washington University and RTOG 93-11 clinical trial data (events/patients: 120/374, WUSTL = 101/237, RTOG9311 = 19/137). Statistical model building was performed based on dosimetric and clinical parameters (patient age, sex, weight loss, pretreatment chemotherapy, concurrent chemo-therapy, fraction size). Awide range of dose–volume parameters were extracted from dearchived treatment plans, including Dx, Vx, MOHx (mean of hottest x% volume), MOCx (mean of coldest x% volume), and gEUD (generalized equivalent uniform dose) values. The most significant single parameters for predicting acute esophagitis (RTOG Grade 2 or greater) were MOH85, mean esophagus dose (MED), and V30. A superior–inferior weighted dose-center position was derived but not found to be significant. Fraction size was found to be significant on univariate logistic analysis (Spearman R = 0.421, p < 0.00001) but not multivariate logistic modeling. Cross-validation model building was used to determine that an optimal model size needed only two parameters (MOH85 and concurrent chemotherapy, robustly selected on bootstrap model-rebuilding). Mean esophagus dose (MED) is preferred instead of MOH85, as it gives nearly the same statistical performance and is easier to compute. AE risk is given as a logistic function of (0.0688 * MED+1.50 * ConChemo-3.13), where MED is in Gy and ConChemo is either 1 (yes) if concurrent chemotherapy was given, or 0 (no). This model correlates to the observed risk of AE with a Spearman coefficient of 0.629 (p < 0.000001). Multivariate statistical model building with cross-validation suggests that a two-variable logistic model based on mean dose and the use of concurrent chemotherapy robustly predicts acute esophagitis risk in combined-data WUSTL and RTOG 93-11 trial datasets.
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影响因子：
4.3
作者：
Qiao, Wen-Bo;Zhao, Yan-Hui;Wang, Rui-Zhi
通讯作者：
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影响因子：
3.8
作者：
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影响因子：
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DOI：
10.1016/j.ijrobp.2004.06.014
发表时间：
2005-02-01
影响因子：
7
作者：
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通讯作者：
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影响因子：
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作者：
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