Abstract：
UNASSIGNED: Selecting the better techniques to harbor optimal motion management, either a stereotactic linear accelerator delivery using TrueBeam (TBX) or Magnetic Resonance (MR)-guided gated delivery using MRIdian (MRG), is time-consuming and costly. To address this challenge, we aimed to develop a decision-supporting algorithm based on a combination of deep learning-generated dose distributions and clinical data.
UNASSIGNED: We retrospectively analyzed 65 patients with liver or pancreatic cancer who underwent both TBX and MRG simulations and planning process. We trained three-dimensional U-Net deep learning models to predict dose distributions and generated dose volume histograms (DVHs) for each system. We integrated predicted DVH metrics into a Bayesian network (BN) model incorporating clinical data.
UNASSIGNED: The MRG prediction model outperformed the TBX model, demonstrating statistically significant superiorities in predicting normalized dose to the PTV and liver. We developed a final BN prediction model integrating the predictive DVH metrics with patient factors like age, PTV size, and tumor location. This BN model an area under the receiver operating characteristic curve index of 83.56%. The decision tree derived from the BN model showed that the tumor location (abutting vs. apart of PTV to hollow viscus organs) was the most important factor to determine TBX or MRG.
UNASSIGNED: We demonstrated a decision-supporting algorithm for selecting optimal RT plans in upper gastrointestinal cancers, incorporating both deep learning-based dose prediction and BN-based treatment selection. This approach might streamline the decision-making process, saving resources and improving treatment outcomes for patients undergoing RT.
UNASSIGNED: We retrospectively analyzed 65 patients with liver or pancreatic cancer who underwent both TBX and MRG simulations and planning process. We trained three-dimensional U-Net deep learning models to predict dose distributions and generated dose volume histograms (DVHs) for each system. We integrated predicted DVH metrics into a Bayesian network (BN) model incorporating clinical data.
UNASSIGNED: The MRG prediction model outperformed the TBX model, demonstrating statistically significant superiorities in predicting normalized dose to the PTV and liver. We developed a final BN prediction model integrating the predictive DVH metrics with patient factors like age, PTV size, and tumor location. This BN model an area under the receiver operating characteristic curve index of 83.56%. The decision tree derived from the BN model showed that the tumor location (abutting vs. apart of PTV to hollow viscus organs) was the most important factor to determine TBX or MRG.
UNASSIGNED: We demonstrated a decision-supporting algorithm for selecting optimal RT plans in upper gastrointestinal cancers, incorporating both deep learning-based dose prediction and BN-based treatment selection. This approach might streamline the decision-making process, saving resources and improving treatment outcomes for patients undergoing RT.
摘要：
■选择更好的技术来进行最佳的运动管理，使用TrueBeam（TBX）的立体定向线性加速器输送或使用MRIdian（MRG）的磁共振（MR）引导门控输送，既耗时又昂贵。为了应对这一挑战，我们旨在开发一种基于深度学习生成的剂量分布和临床数据的决策支持算法.
■我们回顾性分析了65例接受TBX和MRG模拟和计划过程的肝癌或胰腺癌患者。我们训练了三维U-Net深度学习模型，以预测每个系统的剂量分布并生成剂量体积直方图（DVH）。我们将预测的DVH指标整合到结合临床数据的贝叶斯网络（BN）模型中。
■MRG预测模型优于TBX模型，在预测PTV和肝脏的归一化剂量方面显示出统计学上的显着优势。我们开发了一个最终的BN预测模型，将预测DVH指标与患者因素如年龄、PTV尺寸,和肿瘤的位置。该BN模型的接受者工作特性曲线指数下的面积为83.56%。从BN模型得出的决策树显示，肿瘤位置(邻接与除了PTV到中空内脏器官）是确定TBX或MRG的最重要因素。
■我们展示了一种决策支持算法，用于选择上消化道癌症的最佳RT计划，结合基于深度学习的剂量预测和基于BN的治疗选择。这种方法可能会简化决策过程，为接受RT的患者节省资源并改善治疗结果。
■我们回顾性分析了65例接受TBX和MRG模拟和计划过程的肝癌或胰腺癌患者。我们训练了三维U-Net深度学习模型，以预测每个系统的剂量分布并生成剂量体积直方图（DVH）。我们将预测的DVH指标整合到结合临床数据的贝叶斯网络（BN）模型中。
■MRG预测模型优于TBX模型，在预测PTV和肝脏的归一化剂量方面显示出统计学上的显着优势。我们开发了一个最终的BN预测模型，将预测DVH指标与患者因素如年龄、PTV尺寸,和肿瘤的位置。该BN模型的接受者工作特性曲线指数下的面积为83.56%。从BN模型得出的决策树显示，肿瘤位置(邻接与除了PTV到中空内脏器官）是确定TBX或MRG的最重要因素。
■我们展示了一种决策支持算法，用于选择上消化道癌症的最佳RT计划，结合基于深度学习的剂量预测和基于BN的治疗选择。这种方法可能会简化决策过程，为接受RT的患者节省资源并改善治疗结果。
