Dankers Frank, Wijsman Robin, Troost Esther G C, Monshouwer René, Bussink Johan, Hoffmann Aswin L
Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, The Netherlands. Department of Radiation Oncology (MAASTRO clinic), GROW, School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands.
Phys Med Biol. 2017 May 7;62(9):3668-3681. doi: 10.1088/1361-6560/aa5e9e. Epub 2017 Apr 5.
In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC = 0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.
在我们之前的工作中，通过纳入临床参数和平均食管剂量（MED）的多变量逻辑回归分析，建立了一个用于预测非小细胞肺癌（NSCLC）高剂量适形（化疗）放疗后急性食管毒性（AET）≥2级的多变量正常组织并发症概率（NTCP）模型。由于食管是管状器官，食管壁剂量分布的空间信息在预测AET中可能很重要。我们研究了将食管壁剂量-表面数据与空间信息相结合是否能提高我们已建立的NTCP模型的预测能力。对于149例接受高剂量适形放疗的NSCLC患者，生成了食管壁剂量-表面直方图（DSH）和极坐标剂量-表面图（DSM）。DSM用于生成包含剂量-表面分布空间信息的新DSH和剂量-长度直方图。从这些直方图中导出剂量参数，单变量逻辑回归分析表明它们与AET显著相关。按照我们之前的工作，基于单变量分析中最显著的剂量直方图参数（共19个）建立了新的多变量NTCP模型。然而，与基于传统剂量体积数据的已建立的多变量NTCP模型（AUC = 0.84）相比，包含食管壁剂量-表面数据与空间信息的19个新模型并未显示出更好的预测性能（曲线下面积，AUC范围为0.79 - 0.84）。对于AET的预测，基于所提出的多变量统计方法，食管壁剂量分布的空间信息没有附加价值，仅将MED作为预测剂量学参数就足够了。
