基于EUD的鼻咽癌VMAT计划危及器官DVH预测方法  

Dose volume histogram prediction method for organ at risk in VMAT planning of nasopharyngeal carcinoma based on equivalent uniform dose

作　　者：李惠娟 李阳 庄永东 陈仲本 Li Huijuan;Li Yang;Zhuang Yongdong;Chen Zhongben(School of Biomedical Engineering,Guangzhou Xinhua University,Guangzhou 510520,China;Department of Radiation Oncology,National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital&Shenzhen Hospital,Chinese Academy of Medical Sciences and Peking Union Medical College,Shenzhen 518116,China)

机构地区：[1]广州新华学院生物医学工程学院,广州510520 [2]国家癌症中心/国家肿瘤临床医学研究中心/中国医学科学院北京协和医学院肿瘤医院深圳医院放疗科,深圳518116

出　　处：《中华放射肿瘤学杂志》2023年第5期430-437,共8页Chinese Journal of Radiation Oncology

基　　金：国家自然科学基金(U1832128)。

摘　　要：目的评估通过最小化基于等效均匀剂量(EUD)的损失函数优化放疗计划中危及器官(OAR)剂量体积直方图(DVH)预测方法的实用性。方法随机选取2020—2021年在中国医学科学院肿瘤医院深圳医院完成鼻咽癌容积调强弧形治疗(VMAT)的66例患者的治疗计划,其中50例用于训练循环神经网络(RNN)模型,其余16例用于测试模型。研究基于RNN构建了DVH预测模型,并为66例患者均设计了一个9野等权重的三维适形计划。训练时将OAR每个分野对应的DVH作为模型输入,VMAT计划的DVH为预期输出,通过最小化基于EUD的损失函数计算的预测误差训练模型。预测准确度用预测值和真实值之间的平均偏差和标准偏差表示。根据DVH预测结果为测试病例重新优化计划,使用Wilcoxon配对检验和箱线图比较新计划和原计划OAR的EUD和感兴趣DVH参数(如脊髓等串型器官的最大剂量)的一致性和差异性。结果基于EUD的损失函数训练得到的神经网络能够得到更好的DVH预测结果。根据预测DVH得到的新计划与原计划具有很好的一致性:在绝大多数情况下,两组计划的计划靶区(PTV)的D98%都大于95%处方剂量,脑干、脊髓和晶状体的最大剂量和EUD的差异均无统计学意义(P>0.05)。相较于原计划,新计划在视交叉、视神经和眼球的最大剂量平均减少1.56 Gy以上,EUD平均减少1.22 Gy以上,颞叶的最大剂量和EUD分别平均增加了0.60 Gy和0.30 Gy。结论基于EUD的损失函数提高了DVH预测的准确性,确保预测的DVH能够在治疗方案优化中给出适当的剂量目标,并提高计划质量的一致性。Objective To evaluate the practicability of dose volume histogram(DVH)prediction model for organ at risk(OAR)of radiotherapy plan by minimizing the cost function based on equivalent uniform dose(EUD).Methods A total of 66 nasopharyngeal carcinoma(NPC)patients received volume rotational intensity modulated arc therapy(VMAT)at Cancer Hospital&Shenzhen Hospital,Chinese Academy of Medical Sciences from 2020 to 2021 were retrospectively selected for this study.Among them,50 patients were used to train the recurrent neutral network(RNN)model and the remaining 16 cases were used to test the model.DVH prediction model was constructed based on RNN.A three-dimensional equal-weighted 9-field conformal plan was designed for each patient.For each OAR,the DVHs of individual fields were acquired as the model input,and the DVH of VMAT plan was regarded as the expected output.The prediction error obtained by minimizing EUD-based cost function was employed to train the model.The prediction accuracy was characterized by the mean and standard deviation between predicted and true values.The plan was re-optimized for the test cases based on the DVH prediction results,and the consistency and variability of the EUD and DVH parameters of interest(e.g.,maximum dose for serial organs such as the spinal cord)were compared between the re-optimized plan and the original plan of OAR by the Wilcoxon paired test and box line plots.Results The neural network obtained by training the cost function based on EUD was able to obtain better DVH prediction results.The new plan guided by the predicted DVH was in good agreement with the original plan:in most cases,the D98%in the planning target volume(PTV)was greater than 95%of the prescribed dose for both plans,and there was no significant difference in the maximum dose and EUD in the brainstem,spinal cord and lens(all P>0.05).Compared with the original plan,the average reduction of optic chiasm,optic nerves and eyes in the new plans reached more than 1.56 Gy for the maximum doses and more than 1.22 Gy f

关 键 词：鼻咽肿瘤 剂量体积直方图预测 等效均匀剂量 循环神经网络 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] R739.63[自动化与计算机技术—控制科学与工程]