RaySearch Laboratories, Stockholm, Sweden.
Department of Mathematics, KTH Royal Institute of Technology, Stockholm, Sweden.
Med Phys. 2022 Jun;49(6):3564-3573. doi: 10.1002/mp.15622. Epub 2022 Mar 30.
We present a framework for robust automated treatment planning using machine learning, comprising scenario-specific dose prediction and robust dose mimicking.
The scenario dose prediction pipeline is divided into the prediction of nominal dose from input image and the prediction of scenario dose from nominal dose, each using a deep learning model with U-net architecture. By using a specially developed dose-volume histogram-based loss function, the predicted scenario doses are ensured sufficient target coverage despite the possibility of the training data being non-robust. Deliverable plans may then be created by solving a robust dose mimicking problem with the predictions as scenario-specific reference doses.
Numerical experiments are performed using a data set of 52 intensity-modulated proton therapy plans for prostate patients. We show that the predicted scenario doses resemble their respective ground truth well, in particular while having target coverage comparable to that of the nominal scenario. The deliverable plans produced by the subsequent robust dose mimicking were showed to be robust against the same scenario set considered for prediction.
We demonstrate the feasibility and merits of the proposed methodology for incorporating robustness into automated treatment planning algorithms.
我们提出了一个使用机器学习进行稳健自动治疗计划的框架，包括针对特定场景的剂量预测和稳健的剂量模拟。
场景剂量预测流水线分为从输入图像预测名义剂量和从名义剂量预测场景剂量，每个都使用具有 U 形网络架构的深度学习模型。通过使用专门开发的基于剂量-体积直方图的损失函数，即使训练数据可能不稳健，也可以确保预测的场景剂量具有足够的靶区覆盖。然后，可以通过使用预测作为特定于场景的参考剂量来解决稳健的剂量模拟问题来创建可交付的计划。
使用前列腺患者的 52 个强度调制质子治疗计划数据集进行了数值实验。我们表明，预测的场景剂量与其各自的真实情况非常吻合，特别是在具有与名义场景相当的靶区覆盖的情况下。随后通过稳健的剂量模拟生成的可交付计划被证明对预测中考虑的相同场景集具有稳健性。
我们证明了将稳健性纳入自动治疗计划算法的所提出方法的可行性和优点。
