三种智慧放疗计划预测模型的性能评价

doi:10.3760/cma.j.cn113030-20190919-00009

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Abstract Objective To compare the accuracy and generalized robustness of three predictive models of knowledge-based treatment strategies for radiotherapy for optimized model selection. Methods The clinical radiotherapy plans of 45 prostate cancer (PC) cases and 25 nasopharyngeal cancer (NPC) cases were collected, and analyzed using three models (Z,L and S model),proposed by Zhu et al, Appenzoller et al and Shiraishi et al, respectively, to predict the dose-volume histogram (DVH) of bladder and rectum on PC cases and that of left and right parotid on NPC cases. The prediction error was measured by the difference of area under the predicted DVH and the clinical DVH curves (|V(pre_DVH)-V(clin_DVH)|),where a smaller prediction error implies a greater prediction accuracy. The accuracies of these three models were compared on the single organ at risk (OAR), and the generalized robustness of models was evaluated and compared by calculating the standard deviation of the prediction accuracy on different OAR. Results For bladder and rectum, the prediction error of L model (0.114 and 0.163,respectively) was significantly higher than those values of Z and S models (≤0.071,P<0.05);for left parotid gland, the predicted error of S model (0.033) did not present significant difference from those values of Z and L models (≤0.025,P>0.05);for right parotid gland, S model (0.033) demonstrated significantly higher prediction error than those of Z and L models (≤0.028,P<0.05). Regarding different OAR,S model showed a lower standard deviation of prediction accuracy when comparing to Z and L models (0.016,0.018 and 0.060,respectively). Conclusions In the prediction of DVH in bladder and rectum of PC,Z and S models were more accurate than L model. In contrast, Z and L models demonstrated higher accuracy than S model in the prediction of left and right parotid glands of NPC. In respect to different OAR, the generalized robustness of S model was superior than the other two models.

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	Articles by authors
	Wu Aiqian
	Li Yongbao
	Qi Mengke
	Jia Qiyuan
	Guo Futong
	Lu Xingyu
	Liu Yuliang
	Zhou Linghong
	Song Ting
	Chen Chaomin

Key words： Knowledge-based treatment strategy Dose prediction models Accuracy and robustness

Received: 19 September 2019

Fund:Fund programs:National Key Research and Development Project (2017YFC0113203);National Natural Science Foundation of China (81601577,81571771,11805292);Natural Science Foundation of Guangdong, China (2018A0303100020)

Corresponding Authors: Song Ting, Email:tingsong2015@smu.edu.cn;Chen Chaomin, Email:gzccm@fimmu.com

Cite this article:

Wu Aiqian,Li Yongbao,Qi Mengke et al. Evaluation of three predictive models of knowledge-based treatment strategies for radiotherapy[J]. Chinese Journal of Radiation Oncology, 2020, 29(5): 363-373.

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http://journal12.magtechjournal.com/Jweb_fszlx/EN/10.3760/cma.j.cn113030-20190919-00009 OR http://journal12.magtechjournal.com/Jweb_fszlx/EN/Y2020/V29/I5/363

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