Poel Robert, Kamath Amith J, Willmann Jonas, Andratschke Nicolaus, Ermiş Ekin, Aebersold Daniel M, Manser Peter, Reyes Mauricio
Department of Radiation Oncology, Inselspital, Bern University Hospital, University of Bern, CH-3010 Bern, Switzerland.
ARTORG Center for Biomedical Research, University of Bern, CH-3010 Bern, Switzerland.
Cancers (Basel). 2023 Aug 23;15(17):4226. doi: 10.3390/cancers15174226.
External beam radiation therapy requires a sophisticated and laborious planning procedure. To improve the efficiency and quality of this procedure, machine-learning models that predict these dose distributions were introduced. The most recent dose prediction models are based on deep-learning architectures called 3D U-Nets that give good approximations of the dose in 3D almost instantly. Our purpose was to train such a 3D dose prediction model for glioblastoma VMAT treatment and test its robustness and sensitivity for the purpose of quality assurance of automatic contouring. From a cohort of 125 glioblastoma (GBM) patients, VMAT plans were created according to a clinical protocol. The initial model was trained on a cascaded 3D U-Net. A total of 60 cases were used for training, 15 for validation and 20 for testing. The prediction model was tested for sensitivity to dose changes when subject to realistic contour variations. Additionally, the model was tested for robustness by exposing it to a worst-case test set containing out-of-distribution cases. The initially trained prediction model had a dose score of 0.94 Gy and a mean DVH (dose volume histograms) score for all structures of 1.95 Gy. In terms of sensitivity, the model was able to predict the dose changes that occurred due to the contour variations with a mean error of 1.38 Gy. We obtained a 3D VMAT dose prediction model for GBM with limited data, providing good sensitivity to realistic contour variations. We tested and improved the model's robustness by targeted updates to the training set, making it a useful technique for introducing dose awareness in the contouring evaluation and quality assurance process.
外照射放射治疗需要复杂且费力的计划程序。为提高该程序的效率和质量，引入了预测这些剂量分布的机器学习模型。最新的剂量预测模型基于一种称为3D U-Net的深度学习架构，几乎能立即给出三维剂量的良好近似值。我们的目的是训练这样一个用于胶质母细胞瘤容积调强弧形放疗（VMAT）治疗的三维剂量预测模型，并测试其稳健性和敏感性，以用于自动轮廓勾画的质量保证。从125例胶质母细胞瘤（GBM）患者队列中，根据临床方案创建了VMAT计划。初始模型在级联3D U-Net上进行训练。总共60例用于训练，15例用于验证，20例用于测试。当受到实际轮廓变化影响时，测试预测模型对剂量变化的敏感性。此外，通过将模型暴露于包含分布外病例的最坏情况测试集来测试其稳健性。最初训练的预测模型的剂量评分为0.94 Gy，所有结构的平均剂量体积直方图（DVH）评分为1.95 Gy。在敏感性方面，该模型能够预测由于轮廓变化而发生的剂量变化，平均误差为1.38 Gy。我们在有限数据的情况下获得了一个用于GBM的三维VMAT剂量预测模型，对实际轮廓变化具有良好的敏感性。我们通过针对性地更新训练集来测试和提高模型的稳健性，使其成为在轮廓勾画评估和质量保证过程中引入剂量意识的有用技术。
