论文标题
如何应用3D3预测？一种新型的数学模型，以生成帕累托最佳临床适用的IMRT治疗计划，以剂量预测和处方为基础
How to Apply 3D3 Prediction? A Novel Mathematical Model to Generate Pareto Optimal Clinical Applicable IMRT Treatment Plan On the Foundation of Dose Prediction and Prescription
论文作者
论文摘要
在本文中，基于知识的计划已通过新的数学模型进行了革新，该模型将三维剂量分布（3D3）预测转换为临床利用IMRT治疗计划。提出的模型都受益于规定的剂量以及预测的剂量，其目标函数包括二次和线性短语，因此称为Quadlin模型。该模型已在30例从开放KBP数据集中随机选择的头颈癌患者的数据上运行。对于每个患者，此数据库中都有19套剂量预测数据。因此，在CVX框架中总共解决了570个问题，并通过两种计划质量方法评估了结果：1- DVH点差异和2-满足的临床标准。当前研究的结果表明，与数据集的参考计划，3D3预测以及先前研究的结果相比，临床指标的显着改善。因此，与预测的剂量和先前的研究相比，对于570个问题和总ROI，平均而言，临床指标分别提高了21％和15％。
In this paper knowledge based planning has been revolutionized via a novel mathematical model which converts three dimensional dose distribution (3D3) prediction to a clinical utilizable IMRT treatment plan. Presented model has benefited from both prescribed dose as well as predicted dose and its objective function includes both quadratic and linear phrases, so it was called QuadLin model. The model has been run on the data of 30 patients with head and neck cancer randomly selected from the Open KBP dataset. For each patient, there are 19 sets of dose prediction data in this database. Therefore, a total of 570 problems have been solved in the CVX framework and the results have been evaluated by two plan quality approaches: 1- DVH points differences, and 2- satisfied clinical criteria. The results of the current study indicate a strong significant improvement in clinical indicators compared to the reference plan of the dataset, 3D3 predictions, as well as the results of previous researches. Accordingly, on average for 570 problems and total ROIs, clinical indicators have improved by more than 21% and 15% compared to the predicted dose and previous research, respectively.
