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评估哪些剂量函数指标对功能引导放射治疗最为关键。

Evaluating Which Dose-Function Metrics Are Most Critical for Functional-Guided Radiation Therapy.

作者信息

Faught Austin M, Yamamoto Tokihiro, Castillo Richard, Castillo Edward, Zhang Jingjing, Miften Moyed, Vinogradskiy Yevgeniy

机构信息

Department of Radiation Oncology, University of Colorado School of Medicine, Aurora, Colorado.

Department of Radiation Oncology, University of California Davis School of Medicine, Sacramento, California.

出版信息

Int J Radiat Oncol Biol Phys. 2017 Sep 1;99(1):202-209. doi: 10.1016/j.ijrobp.2017.03.051. Epub 2017 Apr 8.

Abstract

PURPOSE

Four-dimensional (4D) computed tomography (CT) ventilation imaging is increasingly being used to calculate lung ventilation and implement functional-guided radiation therapy in clinical trials. There has been little exhaustive work evaluating which dose-function metrics should be used for treatment planning and plan evaluation. The purpose of our study was to evaluate which dose-function metrics best predict for radiation pneumonitis (RP).

METHODS AND MATERIALS

Seventy lung cancer patients who underwent 4D CT imaging and pneumonitis grading were assessed. Pretreatment 4D CT scans of each patient were used to calculate ventilation images. We evaluated 3 types of dose-function metrics that combined the patient's 4D CT ventilation image and treatment planning dose distribution: (1) structure-based approaches; (2) image-based approaches using the dose-function histogram; and (3) nonlinear weighting schemes. Log-likelihood methods were used to generate normal tissue complication probability models predicting grade 3 or higher (ie, grade 3+) pneumonitis for all dose-function schemes. The area under the curve (AUC) was used to assess the predictive power of the models. All techniques were compared with normal tissue complication probability models based on traditional, total lung dose metrics.

RESULTS

The most predictive models were structure-based approaches that focused on the volume of functional lung receiving ≥20 Gy (AUC, 0.70). Probabilities of grade 3+ RP of 20% and 10% correspond to V20 (percentage of volume receiving ≥20 Gy) to the functional subvolumes of 26.8% and 9.3%, respectively. Imaging-based analysis with the dose-function histogram and nonlinear weighted ventilation values yielded AUCs of 0.66 and 0.67, respectively, when we evaluated the percentage of functionality receiving ≥20 Gy. All dose-function metrics outperformed the traditional dose metrics (mean lung dose, AUC of 0.55).

CONCLUSIONS

A full range of dose-function metrics and functional thresholds was examined. The calculated AUC values for the most predictive functional models occupied a narrow range (0.66-0.70), and all showed notable improvements over AUC from traditional lung dose metrics (0.55). Identifying the combinations most predictive of grade 3+ RP provides valuable data to inform the functional-guided radiation therapy process.

摘要

目的

在临床试验中,四维(4D)计算机断层扫描(CT)通气成像越来越多地用于计算肺通气并实施功能引导放射治疗。对于治疗计划和计划评估应使用哪些剂量功能指标,目前尚未有详尽的研究。本研究的目的是评估哪些剂量功能指标能最好地预测放射性肺炎(RP)。

方法和材料

对70例接受4D CT成像和肺炎分级的肺癌患者进行评估。使用每位患者的治疗前4D CT扫描来计算通气图像。我们评估了3种将患者的4D CT通气图像与治疗计划剂量分布相结合的剂量功能指标:(1)基于结构的方法;(2)使用剂量功能直方图的基于图像的方法;(3)非线性加权方案。使用对数似然方法生成预测所有剂量功能方案中3级或更高等级(即3级+)肺炎的正常组织并发症概率模型。曲线下面积(AUC)用于评估模型的预测能力。将所有技术与基于传统全肺剂量指标的正常组织并发症概率模型进行比较。

结果

预测性最强的模型是基于结构的方法,该方法侧重于接受≥20 Gy照射的功能性肺体积(AUC,0.70)。3级+ RP的概率为20%和10%时,分别对应于接受≥20 Gy照射的功能性子体积的V20(体积百分比)为26.8%和9.3%。当我们评估接受≥20 Gy照射的功能百分比时,使用剂量功能直方图和非线性加权通气值的基于图像的分析得出的AUC分别为0.66和0.67。所有剂量功能指标均优于传统剂量指标(平均肺剂量,AUC为0.55)。

结论

研究了一系列剂量功能指标和功能阈值。预测性最强的功能模型的计算AUC值范围较窄(0.66 - 0.70),并且与传统肺剂量指标的AUC(0.55)相比均有显著改善。确定最能预测3级+ RP的组合可为功能引导放射治疗过程提供有价值的数据。

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