文章：

基于深度学习的胶质母细胞瘤剂量预测器——评估剂量意识在轮廓勾画中的敏感性与稳健性

Deep-Learning-Based Dose Predictor for Glioblastoma–Assessing the Sensitivity and Robustness for Dose Awareness in Contouring

原文发布日期：23 August 2023

DOI: 10.3390/cancers15174226

类型: Article

开放获取: 是

英文摘要：

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.

摘要翻译： 

外照射放射治疗需要复杂且耗时的计划制定流程。为提升该流程的效率与质量，能够预测剂量分布的机器学习模型被引入应用。最新的剂量预测模型基于被称为3D U-Net的深度学习架构，可在三维空间中近乎即时地提供精确的剂量近似值。本研究旨在训练针对胶质母细胞瘤容积旋转调强放疗的3D剂量预测模型，并测试其在自动勾画质量保证应用中的鲁棒性与敏感性。基于125例胶质母细胞瘤患者队列，按照临床方案制定容积旋转调强放疗计划。初始模型采用级联式3D U-Net架构进行训练，其中60例用于训练集，15例用于验证集，20例用于测试集。通过模拟实际临床勾画变异，测试了预测模型对剂量变化的敏感性。此外，通过构建包含分布外病例的最坏情况测试集，检验了模型的鲁棒性。初始训练完成的预测模型剂量评分达0.94戈瑞，所有结构的平均剂量体积直方图评分为1.95戈瑞。在敏感性方面，该模型预测勾画变异所致剂量变化的平均误差为1.38戈瑞。本研究利用有限数据成功构建了胶质母细胞瘤3D容积旋转调强放疗剂量预测模型，该模型对实际勾画变异具有良好的敏感性。通过针对性更新训练集，我们测试并提升了模型的鲁棒性，使其成为在勾画评估与质量保证流程中引入剂量感知的有效技术手段。

原文链接：

Deep-Learning-Based Dose Predictor for Glioblastoma–Assessing the Sensitivity and Robustness for Dose Awareness in Contouring