Department of Radiation Oncology, Hiroshima University Hospital, Hiroshima, Japan.
Department of Radiation Oncology, Radboud University Medical Center, Nijmegen, the Netherlands.
Phys Med. 2020 Dec;80:167-174. doi: 10.1016/j.ejmp.2020.10.028. Epub 2020 Nov 11.
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.
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.
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.
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.
在容积调强弧形治疗中，由于肿瘤在不同病例中的位置和大小存在差异，需要进行大量的手动操作，因此缺乏参考剂量分布是治疗计划制定面临的挑战之一。本研究使用两种独立的方法生成预测剂量分布，并比较了基于预测分布的治疗计划与临床值的差异，评估了其疗效。
回顾性采集了 81 例口咽或下咽肿瘤患者的 CT 扫描图像。使用改进的滤波反投影（mFBP）和分层密集连接 U 网（HD-Unet）确定预测剂量分布。从预测分布中提取优化参数，使用商业治疗计划系统获得优化后的剂量分布。
在 10 例患者的测试数据中，mFBP 与临床计划的脑干、脊髓最大剂量和喉平均剂量之间存在显著差异。HD-Unet 剂量分布与临床计划的左腮腺平均剂量之间存在显著差异。在这两种情况下，肿瘤靶区的等效覆盖和均匀性均符合临床计划。
使用两种方法生成了预测剂量分布。在 mFBP 方法中，不需要深度学习等预先学习，因此即使在缺乏足够训练数据的部位，也能提高治疗计划的准确性和效率。
