鼻咽癌IMRT计划腮腺剂量预测模型建立与验证

doi:10.3760/cma.j.issn.1004-4221.2016.02.014

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Abstract Objective To study the mathematical predicting model of parotid DVH for the NPC IMRT planning,and its accuracy with the analysis of medical data. Methods 50 NPC radiotherapy treatment plans with same beam setup were chosen as sample data set, then their parotid DVHs and distance of voxels in the parotid to the target volumes were calculated with self-developed program to form the distance to target histogram (DTHs);principal component analysis was applied to DVHs and DTHs to acquire their principal components (PCs),and then nonlinear multiple variable regression was used to model correlation between the DTHs’ PCs, parotids volume, PTVs and the DVHs. Another 10 plans were chosen as test data set to evaluate the efficacy and accuracy of the final model by comparing the DVHs calculated from our model with those calculated from the TPS. Results Up to 97% information of DTHs and DVHs can be represented with 2 to 3 components, the average fitting error of sample data set was (0±3.5)%;in the 10 test cases, the shapes of DVH curves calculated from predicting model was highly the same with those from the TPS, the average modeling error was (-0.7±4.4)%,the accuracy of prediction was up 95%. Conclusions Our developed model can be used as a quality evaluating tool to predict and assure the dose distribution in parotid of NPC radiotherapy treatment planning effectively and accurately.

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	Articles by authors
	Huang Botian
	Zhu Jinhan
	Yan Xin
	Liu Boji
	Hu Jiang
	Qi Zhenyu

Key words： Mathematical predicting model of parotid Distance to target histogram Dose volume histogram
Fund program:National Science Fund of China Project (81371710) Guangdong Science and Technology Plan Items (2013B021800149)

Received: 11 June 2015

Corresponding Authors: Qi Zhenyu, Email:qizhy@sysucc.org.cn

Cite this article:

Huang Botian,Zhu Jinhan,Yan Xin et al. Development and evaluation of a predicting model of dose volume histograms of parotid in NPC IMRT planning[J]. Chinese Journal of Radiation Oncology, 2016, 25(2): 160-163.

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http://journal12.magtechjournal.com/Jweb_fszlx/EN/10.3760/cma.j.issn.1004-4221.2016.02.014 OR http://journal12.magtechjournal.com/Jweb_fszlx/EN/Y2016/V25/I2/160

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