Radiotherapy treatment planning dose prediction can be used to ensure plan quality and guide automatic plan. One of the dose prediction methods is incorporating historical treatment planning data into algorithms to estimate the dose–volume histogram (DVH) of organ for new patients. Although DVH is used extensively in treatment plan quality and radiotherapy prognosis evaluation, three-dimensional dose distribution can describe the radiation effects more explicitly. The purpose of this retrospective study was to predict the dose distribution of breast cancer radiotherapy by means of deformable registration into atlas images with historical treatment planning data that were considered to achieve expert level. The atlas cohort comprised 20 patients with left-sided breast cancer, previously treated by volumetric-modulated arc radiotherapy. The registration-based prediction technique was applied to 20 patients outside the atlas cohort. This study evaluated and compared three different approaches: registration to the most similar image from a dataset of individual atlas images (SIM), registration to all images from a database of individual atlas images with the average method (WEI_A), and the weighted method (WEI_F). The dose prediction performance of each strategy was quantified using nine metrics, including the region of interest dose error, 80% and 100% prescription area dice similarity coefficients (DSCs), and γ metrics. A Friedman test and a nonparametric exact Wilcoxon signed rank test were performed to compare the differences among groups. The clinical doses of all cases served as the gold standard. The WEI_F method could achieve superior dose prediction results to those of WEI_A. WEI_F outperformed SIM in the organ-at-risk mean absolute difference (MAD). When using the WEI_F method, the MAD values for the ipsilateral lung, heart, and whole lung were 197.9 ± 42.9, 166 ± 55.1, 122.3 ± 25.5, and 55.3 ± 42.2 cGy, respectively. Moreover, SIM exhibited superior prediction in the DSC and γ metrics. When using the SIM method, the means of the 80% and 100% prescription area DSCs, 33γ metric, and 55γ metric were 0.85 ± 0.05, 0.84 ± 0.05, 0.64 ± 0.13, and 0.84 ± 0.10, respectively. The plan target volume and spinal cord MAD when using SIM and WEI were 235.6 ± 158.4 cGy versus 227.4 ± 144.0 cGy ($$p > 0.05$$) and 61.4 ± 44.9 cGy versus 55.3 ± 42.2 cGy ($$p > 0.05$$), respectively. A predicted dose distribution that approximated the clinical plan could be generated using the methods presented in this study.

中文翻译：

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使用可变形图像配准预测乳腺癌的放射治疗剂量分布。

放疗治疗计划剂量预测可用于确保计划质量和指导自动计划。剂量预测方法之一是将历史治疗计划数据合并到算法中，以估计新患者的器官剂量-体积直方图（DVH）。尽管DVH被广泛用于治疗计划质量和放疗预后评估中，但三维剂量分布可以更清晰地描述放射效应。这项回顾性研究的目的是通过将具有历史治疗计划数据的可变形配准到图集图像中来预测乳腺癌放疗的剂量分布，并考虑达到专家水平的历史治疗计划数据。该图集队列包括20例左侧乳腺癌患者，这些患者先前已通过容积调制弧线放射治疗。基于注册的预测技术已应用于图集队列之外的20名患者。这项研究评估并比较了三种不同的方法：从单个地图集图像（SIM）的数据集中向最相似的图像进行配准；通过平均方法（WEI_A）向单个地图集图像的数据库中的所有图像进行配准；以及加权方法（ WEI_F）。使用九种度量标准对每种策略的剂量预测性能进行了量化，包括感兴趣区域剂量误差，80％和100％处方面积骰子相似性系数（DSC）和γ度量标准。进行了Friedman检验和非参数精确Wilcoxon符号秩检验，以比较各组之间的差异。所有病例的临床剂量均作为金标准。WEI_F方法可以获得比WEI_A更好的剂量预测结果。WEI_F在有风险器官的平均绝对差（MAD）方面优于SIM。当使用WEI_F方法时，同侧肺，心脏和全肺的MAD值分别为197.9±42.9、166±55.1、122.3±25.5和55.3±42.2 cGy。此外，SIM在DSC和γ指标方面表现出优异的预测。当使用SIM方法时，80％和100％处方面积DSC的均值分别为0.85±0.05、0.84±0.05、0.64±0.13和0.84±0.10。使用SIM和WEI时的计划目标体积和脊髓MAD分别为235.6±158.4 cGy和227.4±144.0 cGy（$$ p> 0.05 $$）和61.4±44.9 cGy和55.3±42.2 cGy（$ p> 0.05 $$ ）， 分别。