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利用临床、剂量学和空间参数对放射性肺炎风险进行建模。

Modeling radiation pneumonitis risk with clinical, dosimetric, and spatial parameters.

作者信息

Hope Andrew J, Lindsay Patricia E, El Naqa Issam, Alaly James R, Vicic Milos, Bradley Jeffrey D, Deasy Joseph O

机构信息

Department of Radiation Oncology, Washington University School of Medicine, Siteman Cancer Center, St. Louis, Missouri 63110-1032, USA.

出版信息

Int J Radiat Oncol Biol Phys. 2006 May 1;65(1):112-24. doi: 10.1016/j.ijrobp.2005.11.046.

Abstract

PURPOSE

To determine the clinical, dosimetric, and spatial parameters that correlate with radiation pneumonitis.

METHODS AND MATERIALS

Patients treated with high-dose radiation for non-small-cell lung cancer with three-dimensional treatment planning were reviewed for clinical information and radiation pneumonitis (RP) events. Three-dimensional treatment plans for 219 eligible patients were recovered. Treatment plan information, including parameters defining tumor position and dose-volume parameters, was extracted from non-heterogeneity-corrected dose distributions. Correlation to RP events was assessed by Spearman's rank correlation coefficient (R). Mathematical models were generated that correlate with RP.

RESULTS

Of 219 patients, 52 required treatment for RP (median interval, 142 days). Tumor location was the most highly correlated parameter on univariate analysis (R = 0.24). Multiple dose-volume parameters were correlated with RP. Models most frequently selected by bootstrap resampling included tumor position, maximum dose, and D35 (minimum dose to the 35% volume receiving the highest doses) (R = 0.28). The most frequently selected two- or three-parameter models outperformed commonly used metrics, including V20 (fractional volume of normal lung receiving >20 Gy) and mean lung dose (R = 0.18).

CONCLUSIONS

Inferior tumor position was highly correlated with pneumonitis events within our population. Models that account for inferior tumor position and dosimetric information, including both high- and low-dose regions (D(35), International Commission on Radiation Units and Measurements maximum dose), risk-stratify patients more accurately than any single dosimetric or clinical parameter.

摘要

目的

确定与放射性肺炎相关的临床、剂量学和空间参数。

方法与材料

回顾采用三维治疗计划对非小细胞肺癌进行高剂量放疗的患者的临床信息和放射性肺炎(RP)事件。获取了219例符合条件患者的三维治疗计划。从未经非均匀性校正的剂量分布中提取治疗计划信息,包括定义肿瘤位置的参数和剂量体积参数。通过Spearman等级相关系数(R)评估与RP事件的相关性。生成了与RP相关的数学模型。

结果

219例患者中,52例因RP需要治疗(中位间隔时间为142天)。单因素分析中,肿瘤位置是相关性最高的参数(R = 0.24)。多个剂量体积参数与RP相关。通过自助重采样最常选择的模型包括肿瘤位置、最大剂量和D35(接受最高剂量的35%体积的最小剂量)(R = 0.28)。最常选择的两参数或三参数模型优于常用指标,包括V20(接受>20 Gy的正常肺组织的体积分数)和平均肺剂量(R = 0.18)。

结论

在我们的研究人群中,肿瘤下叶位置与肺炎事件高度相关。考虑肿瘤下叶位置和剂量学信息(包括高剂量和低剂量区域(D(35),国际辐射单位与测量委员会最大剂量))的模型比任何单一的剂量学或临床参数更能准确地对患者进行风险分层。

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