ABSTRACT

Purpose: To investigate region-specific models for organ’s three-dimensional dose distribution prediction with neural network.

Methods: The dose distribution from different bladder regions for 52 prostate volumetric modulated arc therapy cases were first analyzed, the two region-specific models were then built to predict the bladder dose distribution, the initial model and the refined model. For the initial model, the bladder was divided into overlapping region and nonoverlapping region, two artificial neural networks were established with each one corresponding to one region. For the refined model, the nonoverlapping region was further divided into three subregions, and four artificial neural network models were built in total. For each artificial neural network model, several spatial and volumetric features for the bladder were extracted as the input to the neural network. To investigate the feasibility and dose distribution prediction accuracy of the proposed two region-specific models, the mean absolute error, gamma passing rate, dose volume histogram, and dose distribution for the refined model were compared.

Results: According to the predicted dose from the initial model and the refined model, the average mean absolute error for all cases is reduced from 5.03 Gy in the initial model to 3.23 Gy in the refined model, the refined model reduce the mean absolute error about 2% relative to the prescription dose. The average area deviation of predicted dose volume histogram by the refined model is 5%, and the average gamma passing rate is 82% and 94% with the 3 mm/3% and 5 mm/5% criteria, which shows that the refined model proposed in this study has high dose-prediction accuracy.

Conclusions: Two region-specific three-dimensional dose distribution prediction models for volumetric modulated arc therapy prostate cases based on neural network have been investigated, the models have shown that a more refined consideration of structures improved the accuracy of predicted dose distribution.

摘要

目的：研究用神经网络预测器官三维剂量分布的区域特定模型。

方法：首先分析了52例前列腺容积调制弧光治疗案例中不同膀胱区域的剂量分布，然后建立了两个区域特定模型来预测膀胱剂量分布，即初始模型和精确模型。对于初始模型，将膀胱分为重叠区域和非重叠区域，建立两个人工神经网络，每个人工神经网络对应一个区域。对于改进的模型，将不重叠的区域进一步划分为三个子区域，并且总共构建了四个人工神经网络模型。对于每个人工神经网络模型，提取了膀胱的一些空间和体积特征作为神经网络的输入。

结果：根据初始模型和精确模型的预测剂量，所有情况下的平均平均绝对误差从初始模型中的5.03 Gy降低到精确模型中的3.23 Gy，精确模型将平均绝对误差降低了相对于处方剂量的2％。改进模型的预测剂量体积直方图的平均面积偏差为5％，在3mm / 3％和5mm / 5％的标准下，平均伽玛通过率分别为82％和94％，这表明改进的模型本研究提出的具有较高的剂量预测准确性。

结论：研究了基于神经网络的容积调制弧光治疗前列腺病例的两个区域特定的三维剂量分布预测模型，这些模型表明，对结构的更精细的考虑提高了预测剂量分布的准确性。