Three-dimensional dose prediction for lung IMRT patients with deep neural networks: robust learning from heterogeneous beam configurations
Three-dimensional dose prediction for lung IMRT patients with deep neural networks: robust learning from heterogeneous beam configurations
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DOI：
10.1002/mp.13597
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发表时间：
2019-08-01
期刊：
影响因子：
3.8
通讯作者：
Jiang, Steve
中科院分区：
文献类型：
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作者：
Barragan-Montero, Ana Maria;Dan Nguyen;Jiang, Steve
目的利用神经网络直接预测三维剂量分布进行自动规划已成为一种流行的方法。然而，现有的方法仅使用患者解剖作为输入，并假设训练数据库中所有患者的光束配置一致。这项工作的目的是开发一个更通用的模型，除了考虑患者解剖结构外，还考虑可变光束配置，以实现更全面的自动规划，并且可能更容易临床实施，而无需为不同的光束设置训练特定的模型。方法基于我们新开发的深度学习架构和层次密集连接的U-Net （HD U-Net）模型，该模型结合了U-Net和DenseNet。AB模型包含10个输入通道：一个用于光束设置，另外9个用于解剖信息（PTV和器官）。光束设置信息由非调制光束的眼视射线追踪剂量分布的三维矩阵表示。我们使用了来自129名接受IMRT治疗的肺癌患者的一组图像，这些图像具有异质光束配置（4-9种不同方向的光束），用于训练/验证（100名患者）和测试（29名患者）。均方误差作为损失函数。我们通过比较预测剂量分布的平均剂量、最大剂量和其他相关的剂量-体积指标与临床给药剂量分布来评估模型的准确性。计算骰子相似系数以解决预测剂量和临床剂量之间等剂量体积的空间对应关系。该模型还与我们之前的研究进行了比较，即仅解剖（AO）模型，该模型不考虑光束设置信息，仅使用9个通道来获取解剖信息。结果AB模型优于AO模型，特别是在中、低剂量区域。就剂量-体积指标而言，AB优于AO约1-2%。在接受5Gy或更高剂量（V-5）时，肺体积的最大改善约为5%。脊髓最大剂量的改善也很重要，交叉验证为3.6%，检测为2.6%。在中、低剂量区域，AB模型获得的等剂量体积Dice分数比AO模型高10%，在高剂量区域，AB模型获得的等剂量体积Dice分数比AO模型高约2-5%。结论不使用光束配置作为输入的AO模型在高剂量区仍能较好地预测剂量分布，但在低剂量区和中剂量区，对于不同光束数和方向的IMRT病例，AO模型的预测误差较大。通过累积非调制光束眼观射线追踪剂量分布考虑光束设置信息，所提出的AB模型在低、中剂量区域显著优于AO模型，在高剂量区域略优于AO模型。这个新模型代表了在实际临床实践中预测3D剂量分布的重要一步，在实际临床实践中，光束配置可能因患者而异，因计划者而异，因机构而异。
Purpose The use of neural networks to directly predict three-dimensional dose distributions for automatic planning is becoming popular. However, the existing methods use only patient anatomy as input and assume consistent beam configuration for all patients in the training database. The purpose of this work was to develop a more general model that considers variable beam configurations in addition to patient anatomy to achieve more comprehensive automatic planning with a potentially easier clinical implementation, without the need to train specific models for different beam settings. Methods The proposed anatomy and beam (AB) model is based on our newly developed deep learning architecture, and hierarchically densely connected U-Net (HD U-Net), which combines U-Net and DenseNet. The AB model contains 10 input channels: one for beam setup and the other 9 for anatomical information (PTV and organs). The beam setup information is represented by a 3D matrix of the non-modulated beam's eye view ray-tracing dose distribution. We used a set of images from 129 patients with lung cancer treated with IMRT with heterogeneous beam configurations (4-9 beams of various orientations) for training/validation (100 patients) and testing (29 patients). Mean squared error was used as the loss function. We evaluated the model's accuracy by comparing the mean dose, maximum dose, and other relevant dose-volume metrics for the predicted dose distribution against those of the clinically delivered dose distribution. Dice similarity coefficients were computed to address the spatial correspondence of the isodose volumes between the predicted and clinically delivered doses. The model was also compared with our previous work, the anatomy only (AO) model, which does not consider beam setup information and uses only 9 channels for anatomical information. Results The AB model outperformed the AO model, especially in the low and medium dose regions. In terms of dose-volume metrics, AB outperformed AO by about 1-2%. The largest improvement was found to be about 5% in lung volume receiving a dose of 5Gy or more (V-5). The improvement for spinal cord maximum dose was also important, that is, 3.6% for cross-validation and 2.6% for testing. The AB model achieved Dice scores for isodose volumes as much as 10% higher than the AO model in low and medium dose regions and about 2-5% higher in high dose regions. Conclusions The AO model, which does not use beam configuration as input, can still predict dose distributions with reasonable accuracy in high dose regions but introduces large errors in low and medium dose regions for IMRT cases with variable beam numbers and orientations. The proposed AB model outperforms the AO model substantially in low and medium dose regions, and slightly in high dose regions, by considering beam setup information through a cumulative non-modulated beam's eye view ray-tracing dose distribution. This new model represents a major step forward towards predicting 3D dose distributions in real clinical practices, where beam configuration could vary from patient to patient, from planner to planner, and from institution to institution.