...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Radiotherapy and oncology: Journal of the European Society for Therapeutic Radiology and Oncology >Attempts to predict the long-term decrease in lung function due to radiotherapy of non-small cell lung cancer.
【24h】

Attempts to predict the long-term decrease in lung function due to radiotherapy of non-small cell lung cancer.

机译：尝试预测由于非小细胞肺癌放疗导致的肺功能长期下降。

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

PURPOSE: To obtain a model which can predict long-term decrease in lung function due to radiation damage from dose-volume data for patients with non-small cell lung cancer. PATIENTS AND METHODS: 27 patients were included, all long-term survivors after radical radiation therapy. For each patient a regression analysis was performed on a post-RT succession of measurements of FEV1 in order to estimate the decrease after 2 years and a standard error (SE) on this regression estimate. The modelling was based on dose-volume histograms (DVH) exported from the treatment planning system, and involved fits of threshold models, a mean lung dose model as well as more complex models based on the relative damaged volume (rdV). RESULTS: Decreases after 2 years of up to 28% in FEV1 was measured (median 10%), with significant day-to-day variation in FEV1 for the individual patient. The threshold models predicted the long-term decrease in FEV1 well when the SE was interpreted as the uncertainty of the measured decrease. The best threshold value, marginally, was 30 Gy with an R(2) of 0.46. The mean lung dose model did not perform so well. A complex model based on rdV performed better than any of the other models (R(2)=0.52). CONCLUSION: The long-term decrease in FEV1 could be predicted from a simple dose-volume model when the SE was interpreted as the uncertainty of the measured decrease.
机译：目的：从非小细胞肺癌患者的剂量-体积数据中获得一种可以预测由于辐射损伤导致肺功能长期下降的模型。病人和方法：纳入27例患者，所有患者均接受根治性放射治疗。对于每位患者，对RTV后FEV1的连续测量进行回归分析，以估计2年后的下降以及该回归估计的标准误（SE）。该建模基于从治疗计划系统导出的剂量-体积直方图（DVH），并涉及阈值模型，平均肺部剂量模型以及基于相对损伤体积（rdV）的更复杂模型的拟合。结果：2年后测得FEV1下降最多28％（中位数为10％），个别患者的FEV1每天变化很大。当将SE解释为测得的下降的不确定性时，阈值模型预测FEV1井的长期下降。最佳阈值略为30 Gy，R（2）为0.46。平均肺部剂量模型表现不佳。基于rdV的复杂模型的性能优于其他任何模型（R（2）= 0.52）。结论：当SE被解释为测量的下降的不确定性时，FEV1的长期下降可以通过简单的剂量-体积模型预测。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号