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头颈部调强放疗计划中危及器官剂量学建模:一项技术间和机构间研究。

Modeling the dosimetry of organ-at-risk in head and neck IMRT planning: an intertechnique and interinstitutional study.

机构信息

Department of Radiation Oncology, The University of North Carolina, Chapel Hill, North Carolina 27599.

出版信息

Med Phys. 2013 Dec;40(12):121704. doi: 10.1118/1.4828788.

Abstract

PURPOSE

To build a statistical model to quantitatively correlate the anatomic features of structures and the corresponding dose-volume histogram (DVH) of head and neck (HN) Tomotherapy (Tomo) plans. To study if the model built upon one intensity modulated radiation therapy (IMRT) technique (such as conventional Linac) can be used to predict anticipated organs-at-risk (OAR) DVH of patients treated with a different IMRT technique (such as Tomo). To study if the model built upon the clinical experience of one institution can be used to aid IMRT planning for another institution.

METHODS

Forty-four Tomotherapy intensity modulate radiotherapy plans of HN cases (Tomo-IMRT) from Institution A were included in the study. A different patient group of 53 HN fixed gantry IMRT (FG-IMRT) plans was selected from Institution B. The analyzed OARs included the parotid, larynx, spinal cord, brainstem, and submandibular gland. Two major groups of anatomical features were considered: the volumetric information and the spatial information. The volume information includes the volume of target, OAR, and overlapped volume between target and OAR. The spatial information of OARs relative to PTVs was represented by the distance-to-target histogram (DTH). Important anatomical and dosimetric features were extracted from DTH and DVH by principal component analysis. Two regression models, one for Tomotherapy plan and one for IMRT plan, were built independently. The accuracy of intratreatment-modality model prediction was validated by a leave one out cross-validation method. The intertechnique and interinstitution validations were performed by using the FG-IMRT model to predict the OAR dosimetry of Tomo-IMRT plans. The dosimetry of OARs, under the same and different institutional preferences, was analyzed to examine the correlation between the model prediction and planning protocol.

RESULTS

Significant patient anatomical factors contributing to OAR dose sparing in HN Tomotherapy plans have been analyzed and identified. For all the OARs, the discrepancies of dose indices between the model predicted values and the actual plan values were within 2.1%. Similar results were obtained from the modeling of FG-IMRT plans. The parotid gland was spared in a comparable fashion during the treatment planning of two institutions. The model based on FG-IMRT plans was found to predict the median dose of the parotid of Tomotherapy plans quite well, with a mean error of 2.6%. Predictions from the FG-IMRT model suggested the median dose of the larynx, median dose of the brainstem and D2 of the brainstem could be reduced by 10.5%, 12.8%, and 20.4%, respectively, in the Tomo-IMRT plans. This was found to be correlated to the institutional differences in OAR constraint settings. Re-planning of six Tomotherapy patients confirmed the potential of optimization improvement predicted by the FG-IMRT model was correct.

CONCLUSIONS

The authors established a mathematical model to correlate the anatomical features and dosimetric indexes of OARs of HN patients in Tomotherapy plans. The model can be used for the setup of patient-specific OAR dose sparing goals and quality control of planning results.The institutional clinical experience was incorporated into the model which allows the model from one institution to generate a reference plan for another institution, or another IMRT technique.

摘要

目的

建立一个统计模型,定量关联头颈部(HN)调强适形放疗(Tomo)计划的解剖结构特征与相应的剂量-体积直方图(DVH)。研究基于一种调强放疗技术(如常规直线加速器)构建的模型是否可用于预测采用不同调强放疗技术(如 Tomo)治疗的患者的预期危及器官(OAR)DVH。研究基于一个机构的临床经验构建的模型是否可用于辅助另一个机构的调强放疗计划。

方法

本研究纳入了来自机构 A 的 44 例 Tomotherapy 调强放疗(Tomo-IMRT)HN 病例的计划,选择了来自机构 B 的 53 例 HN 固定龙门调强放疗(FG-IMRT)计划的不同患者组。分析的 OAR 包括腮腺、喉、脊髓、脑干和颌下腺。考虑了两组主要的解剖特征:体积信息和空间信息。体积信息包括靶区、OAR 和靶区与 OAR 重叠的体积。OAR 相对于 PTV 的空间信息由靶区到 OAR 的距离直方图(DTH)表示。通过主成分分析(PCA)从 DTH 和 DVH 中提取重要的解剖和剂量学特征。分别为 Tomotherapy 计划和 IMRT 计划建立了两个回归模型。通过留一交叉验证方法验证了治疗模式内模型预测的准确性。使用 FG-IMRT 模型对 Tomo-IMRT 计划的 OAR 剂量进行预测,以进行技术间和机构间的验证。分析相同和不同机构偏好下 OAR 剂量的相关性,以检验模型预测与计划方案之间的关系。

结果

分析并确定了对头颈部 Tomotherapy 计划中 OAR 剂量节省有显著影响的患者解剖因素。对于所有 OAR,模型预测值与实际计划值之间的剂量指标差异均在 2.1%以内。从 FG-IMRT 计划的建模中也得到了类似的结果。两个机构的治疗计划都以类似的方式保护了腮腺。基于 FG-IMRT 计划的模型可以很好地预测 Tomotherapy 计划中腮腺的中位数剂量,平均误差为 2.6%。FG-IMRT 模型的预测表明,Tomo-IMRT 计划中喉的中位数剂量、脑干的中位数剂量和脑干 D2 可以分别降低 10.5%、12.8%和 20.4%,这与 OAR 约束设置的机构差异有关。对 6 例 Tomotherapy 患者进行重新计划,证实了 FG-IMRT 模型预测的优化潜力是正确的。

结论

作者建立了一个数学模型来关联 Tomotherapy 计划中头颈部患者的解剖结构特征和 OAR 的剂量学指标。该模型可用于设定患者特异性 OAR 剂量保护目标和计划结果的质量控制。将机构的临床经验纳入模型,允许模型从一个机构生成另一个机构或另一种调强放疗技术的参考计划。

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