Evaluation of optimization workflow using custom-made planning through predicted dose distribution for head and neck tumor treatment
Evaluation of optimization workflow using custom-made planning through predicted dose distribution for head and neck tumor treatment
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DOI：
10.1016/j.ejmp.2020.10.028
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发表时间：
2020-12-01
影响因子：
3.4
通讯作者：
Nagata, Yasushi
中科院分区：
文献类型：
--
作者：
Miki, Kentaro;Kusters, Martijn;Nagata, Yasushi
目的：缺乏参考剂量分布是在体积调制弧治疗中使用的治疗计划中的挑战之一，因为在不同情况下，肿瘤的位置和大小的变化导致许多手动过程。在本研究中，使用两种独立的方法生成预测剂量分布。方法：回顾性分析81例口咽部或下咽部肿瘤患者的CT扫描资料，并将预测分布的治疗计划与临床价值进行比较，评价其疗效。预测的剂量分布使用修改的滤波反投影（mFBP）和分层密集连接的U-网（HD-Unet）。从预测分布中提取优化参数，并使用商业治疗计划系统获得优化的剂量分布。结果：在10例患者的测试数据中，脑干，脊髓的最大剂量和喉的平均剂量，观察到mFBP和临床计划之间的显着差异。对于左侧腮腺的平均剂量，观察到HD-Unet剂量和临床计划的剂量分布之间存在显著差异。在这两种情况下，等效的覆盖率和平坦度的临床计划观察肿瘤target.Conclusions：预测的剂量分布使用两种方法生成。在mFBP方法的情况下，不需要预先学习，例如深度学习;因此，即使对于没有足够训练数据的研究中心，治疗计划的准确性和效率也将得到提高。
Purpose: Lack of a reference dose distribution is one of the challenges in the treatment planning used in volumetric modulated arc therapy because numerous manual processes result from variations in the location and size of a tumor in different cases. In this study, a predicted dose distribution was generated using two independent methods. Treatment planning using the predicted distribution was compared with the clinical value, and its efficacy was evaluated.Methods: Computed tomography scans of 81 patients with oropharynx or hypopharynx tumors were acquired retrospectively. The predicted dose distributions were determined using a modified filtered back projection (mFBP) and a hierarchically densely connected U-net (HD-Unet). Optimization parameters were extracted from the predicted distribution, and the optimized dose distribution was obtained using a commercial treatment planning system.Results: In the test data from ten patients, significant differences between the mFBP and clinical plan were observed for the maximum dose of the brain stem, spinal cord, and mean dose of the larynx. A significant difference between the dose distributions from the HD-Unet dose and the clinical plan was observed for the mean dose of the left parotid gland. In both cases, the equivalent coverage and flatness of the clinical plan were observed for the tumor target.Conclusions: The predicted dose distribution was generated using two approaches. In the case of the mFBP approach, no prior learning, such as deep learning, is required; therefore, the accuracy and efficiency of treatment planning will be improved even for sites where sufficient training data are unavailable.