论文标题
基于深度学习的剂量预测头部和颈部放射治疗计划的自动化，个性化质量保证
Deep Learning-Based Dose Prediction for Automated, Individualized Quality Assurance of Head and Neck Radiation Therapy Plans
论文作者
论文摘要
目的：本研究旨在使用深度学习的剂量预测来评估头颈部（HN）计划质量并确定次优计划。 方法：使用基于Rapidplan知识的计划（KBP）创建了总共245个VMAT HN计划。在HN辐射肿瘤学家的监督下，选择了112个高质量计划的子集。我们培训了3D密集的U-NET体系结构，以使用90个计划中的3倍交叉验证来预测3维剂量分布。模型输入包括CT图像，目标处方以及有风险的目标和器官（OARS）的轮廓。该模型的性能是在其余22个测试计划中评估的。然后，我们测试了剂量预测模型的应用，以自动审查计划质量。预测14个临床计划的剂量分布。将预测的与临床桨剂量指标与使用2 Gy剂量差或3％剂量体积阈值相比的标志桨与具有次优的正常组织的标志桨进行了比较。将3 hn辐射肿瘤学家与手动标志进行比较。 结果：预测的剂量分布与KBP计划具有可比性的质量。目标和KBP规划的D1％，D95％和D99％之间的差异在-2.53％以内（SD = 1.34％），-0.42％（SD = 1.27％）和-0.12％和-0.12％和-0.12％和-0.12％（分别为SD = 1.97％），以及OAR均值和最大剂量和最高剂量= 1. 4.33 gy和0.33 gy（SD）（SD）（SD）（SD）（SD）（SD）（SD）（SD）（SD） -0.96GY（SD = 2.08）。在计划质量评估研究中，放射肿瘤学家标记了47个桨，以改善计划。层间变异性很高； 83％的医师标志桨仅由3名医生之一标记。比较剂量预测模型标记了63个桨，其中包括47位医师标记的桨中的30个。 结论：深度学习可以预测高质量的剂量分布，可以用作对HN计划质量的自动化，个性化评估的比较剂量分布。
Purpose: This study aimed to use deep learning-based dose prediction to assess head and neck (HN) plan quality and identify suboptimal plans. Methods: A total of 245 VMAT HN plans were created using RapidPlan knowledge-based planning (KBP). A subset of 112 high-quality plans was selected under the supervision of an HN radiation oncologist. We trained a 3D Dense Dilated U-Net architecture to predict 3-dimensional dose distributions using 3-fold cross-validation on 90 plans. Model inputs included CT images, target prescriptions, and contours for targets and organs at risk (OARs). The model's performance was assessed on the remaining 22 test plans. We then tested the application of the dose prediction model for automated review of plan quality. Dose distributions were predicted on 14 clinical plans. The predicted versus clinical OAR dose metrics were compared to flag OARs with suboptimal normal tissue sparing using a 2 Gy dose difference or 3% dose-volume threshold. OAR flags were compared to manual flags by 3 HN radiation oncologists. Results: The predicted dose distributions were of comparable quality to the KBP plans. The differences between the predicted and KBP-planned D1%, D95%, and D99% across the targets were within -2.53%(SD=1.34%), -0.42%(SD=1.27%), and -0.12%(SD=1.97%), respectively, and the OAR mean and maximum doses were within -0.33Gy(SD=1.40Gy) and -0.96Gy(SD=2.08Gy). For the plan quality assessment study, radiation oncologists flagged 47 OARs for possible plan improvement. There was high interphysician variability; 83% of physician-flagged OARs were flagged by only one of 3 physicians. The comparative dose prediction model flagged 63 OARs, including 30 of 47 physician-flagged OARs. Conclusion: Deep learning can predict high-quality dose distributions, which can be used as comparative dose distributions for automated, individualized assessment of HN plan quality.
