Zhuang Yongdong, Xie Yaoqin, Wang Luhua, Huang Shaomin, Chen Li-Xin, Wang Yuenan
Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China.
Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Biomed Res Int. 2021 Jan 19;2021:2043830. doi: 10.1155/2021/2043830. eCollection 2021.
A recurrent neural network (RNN) and its variants such as gated recurrent unit-based RNN (GRU-RNN) were found to be very suitable for dose-volume histogram (DVH) prediction in our previously published work. Using the dosimetric information generated by nonmodulated beams of different orientations, the GRU-RNN model was capable of accurate DVH prediction for nasopharyngeal carcinoma (NPC) treatment planning. On the basis of our previous work, we proposed an improved approach and aimed to further improve the DVH prediction accuracy as well as study the feasibility of applying the proposed method to relatively small-size patient data.
Eighty NPC volumetric modulated arc therapy (VMAT) plans with local IRB's approval in recent two years were retrospectively and randomly selected in this study. All these original plans were created using the Eclipse treatment planning system (V13.5, Varian Medical Systems, USA) with ≥95% of PGTVnx receiving the prescribed doses of 70 Gy, ≥95% of PGTVnd receiving 66 Gy, and ≥95% of PTV receiving 60 Gy. Among them, fifty plans were used to train the DVH prediction model, and the remaining were used for testing. On the basis of our previously published work, we simplified the 3-layer GRU-RNN model to a single-layer model and further trained every organ at risk (OAR) separately with an OAR-specific equivalent uniform dose- (EUD-) based loss function.
The results of linear least squares regression obtained by the new proposed method showed the excellent agreements between the predictions and the original plans with the correlation coefficient = 0.976 and 0.968 for EUD results and maximum dose results, respectively, and the coefficient of our previously published method was 0.957 and 0.946, respectively. The Wilcoxon signed-rank test results between the proposed and the previous work showed that the proposed method could significantly improve the EUD prediction accuracy for the brainstem, spinal cord, and temporal lobes with a value < 0.01.
The accuracy of DVH prediction achieved in different OARs showed the great improvements compared to the previous works, and more importantly, the effectiveness and robustness showed by the simplified GRU-RNN trained from relatively small-size DVH samples, fully demonstrated the feasibility of applying the proposed method to small-size patient data. Excellent agreements in both EUD results and maximum dose results between the predictions and original plans indicated the application prospect in a physically and biologically related (or a mixture of both) model for treatment planning.
在我们之前发表的工作中发现，递归神经网络（RNN）及其变体，如基于门控循环单元的RNN（GRU-RNN），非常适合剂量体积直方图（DVH）预测。利用不同方向的非调制射束生成的剂量学信息，GRU-RNN模型能够对鼻咽癌（NPC）治疗计划进行准确的DVH预测。基于我们之前的工作，我们提出了一种改进方法，旨在进一步提高DVH预测准确性，并研究将该方法应用于相对小尺寸患者数据的可行性。
本研究回顾性随机选取了近两年经当地机构审查委员会批准的80例NPC容积调强弧形放疗（VMAT）计划。所有这些原始计划均使用Eclipse治疗计划系统（V13.5，瓦里安医疗系统公司，美国）创建，≥95%的鼻咽部原发肿瘤（PGTVnx）接受70 Gy的处方剂量，≥95%的咽后淋巴结转移灶（PGTVnd）接受66 Gy，≥95%的计划靶区（PTV）接受60 Gy。其中，50个计划用于训练DVH预测模型，其余计划用于测试。基于我们之前发表的工作，我们将3层GRU-RNN模型简化为单层模型，并使用基于特定危及器官（OAR）等效均匀剂量（EUD）的损失函数分别对每个OAR进行进一步训练。
新提出的方法通过线性最小二乘回归得到的结果表明，预测结果与原始计划之间具有良好的一致性，EUD结果和最大剂量结果的相关系数分别为0.976和0.968，而我们之前发表的方法的相关系数分别为0.957和0.946。所提出方法与之前工作之间的Wilcoxon符号秩检验结果表明，所提出的方法能够显著提高脑干、脊髓和颞叶的EUD预测准确性，P值<0.01。
与之前的工作相比，不同OAR中DVH预测的准确性有了很大提高，更重要的是，从相对小尺寸的DVH样本训练得到的简化GRU-RNN所显示的有效性和稳健性，充分证明了将所提出方法应用于小尺寸患者数据的可行性。预测结果与原始计划在EUD结果和最大剂量结果方面的良好一致性表明了其在物理和生物学相关（或两者混合）治疗计划模型中的应用前景。
