中华放射医学与防护杂志

陈辛元,杨继明,易俊林,戴建荣.基于人工神经网络模型的鼻咽癌VMAT计划质量控制方法[J].中华放射医学与防护杂志,2020,40(2):99-105

基于人工神经网络模型的鼻咽癌VMAT计划质量控制方法

Quality control of VMAT planning using artificial neural network models for nasopharyngeal carcinoma

投稿时间：2019-08-27

DOI：10.3760/cma.j.issn.0254-5098.2020.02.005

英文关键词:Radiotherapy planning Dose prediction Artificial neural network Quality control Nasopharyngeal carcinoma

基金项目:北京市科学技术委员会医药协同科技创新研究（Z181100001918002）；科技部国家重点研发计划项目（2017YFC0107500）；中国癌症基金会北京希望马拉松专项基金（LC2018A14）；国家自然科学基金（11605291，11475261）

作者	单位	E-mail
陈辛元	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, 北京 100021
杨继明	宁波市第一医院放化疗中心 315010
易俊林	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, 北京 100021
戴建荣	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, 北京 100021	dai_jianrong@cicams.ac.cn

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中文摘要:

目的训练放疗计划个体化三维剂量预测模型，并使用该模型建立计划质量控制方法。方法回顾性分析99例已临床实施的早期鼻咽癌同步加量容积旋转调强放疗（VMAT）计划，提取7个几何特征，包括各危及器官（OARs）到PTV、加量靶区和外轮廓的最小距离，及4个坐标位置关系特征。训练（89例）并验证（10例）基于人工神经网络（ANN）的三维剂量分布预测模型；然后基于该预测模型建立放疗计划质量控制方法。以各危及器官剂量学参数D_2%、D_25%、D_50%、D_75%和平均剂量（MD）为质量控制指标，通过标准为人工计划和预测剂量差别≤ 10%。采用由低年资物理师设计的10例计划，对该质量控制方法进行测试。结果 18个头颈部OARs的主要剂量学指标，预测剂量与专家计划结果差异无统计学意义。剂量预测结果与专家计划相比，D_2%、D_25%、D_50%、D_75%和平均剂量（MD）的差别均控制在1.2 Gy以内。由低年资物理师设计的10例计划均达到常规临床剂量限值的要求，而利用建立的质量控制方法检出1例计划的脊髓、脊髓危及器官的计划体积（PRV）、脑干和脑干PRV剂量限制有待改善。根据模型预测值重新优化计划后，脊髓和脑干D_2%分别降低了8.4和5.8 Gy。结论提出了一种简单易行的放疗计划质量控制方法，能克服统一性剂量限值未考虑患者特异性的缺陷，可提高个体化计划质量和稳定性。

英文摘要:

Objective To train individualized three-dimensional (3D) dose prediction models for radiotherapy planning, and use the models to establish a planning quality control method. Methods A total of 99 cases diagnosed as early nasopharyngeal carcinoma (NPC) were analyzed retrospectively, who received simultaneous integrated boost (SIB) with volumetric modulated arc therapy (VMAT). Seven geometric features were extracted, including the minimum distance features from each organs at risk (OARs) to planning target volume (PTV), boost targets and outline, as well as four coordinate position characteristics.89 cases were trained and 10 cases were tested based on 3D dose distribution prediction models using artificial neural network (ANN).A planning quality control method were established based on the prediction models. The dosimetric parameters including D_2%, D_25%, D_50%, D_75% and mean dose (MD) of each OAR were used as quality control indicators, and the passing criteria was defined as that the dosimetric difference between manual planning and the predicted dose should be less than 10%. The quality control method was tested with 10 plans designed by a junior physicist. Results There was no significant discrepancy between the model predicted dose and the result of expert plan in the main dosimetric indexes of 18 OARs. The dose differences of D_2%, D_25%, D_50%, D_75% and MD were all controlled within 1.2 Gy.All the 10 plans designed by a junior physicist reached the general clinical dose requirements, while by using our proposes quality control method, one of these plans was observed not optimal enough and some dosimetric parameters of spinal cord, spinal cord PRV, brainstem and brainstem PRV could be improved. After re-optimizing this plan according to the predicted values of the model, the D_2% of spinal cord and brainstem decreased by 8.4 Gy and 5.8 Gy, respectively. Conclusions This study proposes a simple and convenient quality control method for radiotherapy planning. This method could overcome the disadvantage of unified dose constrains without considering patient-specific conditions, and improve the quality and stability of individualized radiotherapy planning.

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