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Research on KBP model refining method using multi‐criterion optimization technology
Cai Mafan1, Zuo Guoping1, Yang Zhen2, Cao Ying2, Zhang Zijian2, Hu Yongmei2, Yang Xiaoyu2
1School of Nuclear Science and Technology, University of South China, Hengyang 421001, China; 2Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
AbstractObjective Utilizing multi‐criterion optimization (MCO) technology to improve plan design quality based on knowledge‐based planning (KBP) model. Methods Fifty‐five patients with nasopharyngeal carcinoma (NPC) who had completed radiotherapy were selected, and fixed‐field intensity‐modulated radiotherapy (IMRT) technology was used in each case. Among them, 40 cases were randomly selected as training set 1. Then, IMRT plans in training set 1 were preprocessed by MCO technology to construct a new training set 2. With the initial training set 1 and the processed training set 2 as training samples, the traditional KBP model and the MCO‐KBP model refined by MCO technology were trained, respectively. Among the remaining 15 cases, 5 cases were randomly selected as the validation set, and the remaining 10 cases were used as the test set. After verification, the test set was used to statistically analyze the plan quality of the initial manual plan and the automatic plan generated by the traditional KBP model and the MCO‐KBP model. Results The target dose (D95%) of plans generated by the traditional KBP model and the MCO‐KBP model met the clinical requirements. Conformity index (CI) and homogeneity index (HI) were almost the same (P>0.05), and the doses of organ at risk (OAR) of the automatic plans generated by the MCO‐KBP model were lower than those of the traditional KBP model. For example, compared with the traditional KBP model, the average Dmax of the brainstem in the automatic plans generated by the MCO‐KBP model was lower by 2.13 Gy, the average Dmean of the left parotid gland was lower by 1.39 Gy, the average Dmean of the right parotid gland was lower by 1.59 Gy, and the average Dmax of the left optic nerve was lower by 1.42 Gy, the average Dmax of the right optic nerve was lower by 1.16 Gy, and the average Dmax of the pituitary gland was lower by 1.88 Gy. All of the above‐mentioned dosimetry indexes were statistically significant. Conclusion Compared with the traditional KBP model, the IMRT plans designed by the refined MCO‐KBP model have obvious advantages in the protection of OAR, which proves the feasibility of utilizing MCO technology to improve the plan design quality of the KBP model.
Fund:National Natural Science Foundation of China(12005306); Natural Science Foundation of Hunan Province(2021JJ40966)
Corresponding Authors:
Yang Xiaoyu, Email: yxiaoyu@csu.edu.cn
Cite this article:
Cai Mafan,Zuo Guoping,Yang Zhen et al. Research on KBP model refining method using multi‐criterion optimization technology[J]. Chinese Journal of Radiation Oncology, 2022, 31(9): 811-816.
Cai Mafan,Zuo Guoping,Yang Zhen et al. Research on KBP model refining method using multi‐criterion optimization technology[J]. Chinese Journal of Radiation Oncology, 2022, 31(9): 811-816.
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