Department of Radiation Oncology, Osaka University Graduate School of Medicine, 2-2 Yamada-oka, Suita, Osaka 565-0871 Japan.
Department of Radiological Sciences, Faculty of Health Sciences, Komazawa University, 1-23-1 Komazawa, Setagaya-ku, Tokyo 154-8525 Japan.
Phys Med. 2020 Apr;72:88-95. doi: 10.1016/j.ejmp.2020.03.023. Epub 2020 Apr 1.
This study aims to investigate the feasibility of using convolutional neural networks to predict an accurate and high resolution dose distribution from an approximated and low resolution input dose.
Sixty-six patients were treated for prostate cancer with VMAT. We created the treatment plans using the Acuros XB algorithm with 2 mm grid size, followed by the dose calculated using the anisotropic analytical algorithm with 5 mm grid with the same plan parameters. U-net model was used to predict 2 mm grid dose from 5 mm grid dose. We investigated the two models differing for the training data used as input, one used just the low resolution dose (D model) and the other combined the low resolution dose with CT data (DC model). Dice similarity coefficient (DSC) was calculated to ascertain how well the shape of the dose-volume is matched. We conducted gamma analysis for the following: DVH from the two models and the reference DVH for all prostate structures.
The DSC values in the DC model were significantly higher than those in the D model (p < 0.01). For the CTV, PTV, and bladder, the gamma passing rates in the DC model were significantly higher than those in the D model (p < 0.002-0.02). The mean doses in the CTV and PTV for the DC model were significantly better matched to those in the reference dose (p < 0.0001).
The proposed U-net model with dose and CT image used as input predicted more accurate dose.
本研究旨在探讨卷积神经网络从近似低分辨率输入剂量预测准确、高分辨率剂量分布的可行性。
对 66 例前列腺癌患者采用容积旋转调强（VMAT）进行治疗。我们使用 Acuros XB 算法创建了治疗计划，网格尺寸为 2mm，然后使用具有相同计划参数的各向异性分析算法计算网格尺寸为 5mm 的剂量。使用 U-net 模型从 5mm 网格剂量预测 2mm 网格剂量。我们研究了两种模型，其输入数据不同，一种仅使用低分辨率剂量（D 模型），另一种将低分辨率剂量与 CT 数据相结合（DC 模型）。计算了 Dice 相似系数（DSC）以确定剂量-体积形状的匹配程度。我们对以下内容进行了伽马分析：来自两个模型和所有前列腺结构参考剂量体积直方图（DVH）的 DVH。
DC 模型中的 DSC 值明显高于 D 模型（p<0.01）。对于 CTV、PTV 和膀胱，DC 模型中的伽马通过率明显高于 D 模型（p<0.002-0.02）。DC 模型中 CTV 和 PTV 的平均剂量与参考剂量更匹配（p<0.0001）。
本研究提出的 U-net 模型使用剂量和 CT 图像作为输入，可预测更准确的剂量。
