A study of automatic planning for esophageal cancer with intensity-modulated radiotherapy based on dose prediction and beam angle optimization
10.3760/cma.j.cn113030-20200826-00441
- VernacularTitle:基于危及器官剂量预测和射野角度优化的食管癌调强放疗自动计划研究
- Author:
Zhaoyang LOU
1
;
Hongchang LEI
;
Ronghu MAO
;
Wei GUO
;
Bing LI
;
Hong GE
Author Information
1. 郑州大学附属肿瘤医院/河南省肿瘤医院放疗科 450008
- Keywords:
Dose prediction;
Beam angle optimization;
Automatic planning;
Esophageal neoplasm/intensity-modulated radiotherapy
- From:
Chinese Journal of Radiation Oncology
2021;30(12):1275-1279
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To propose an automatic planning method of intensity-modulated radiotherapy (IMRT) for esophageal cancer based on dose volume histogram prediction and beam angle optimization in Raystation treatment planning system.Methods:50 IMRT plans of esophageal cancer were selected as the training set to establish a dose prediction model for organs at risk. Another 20 testing plans were optimized in Raystation using RuiPlan and manual method, and the beam angle optimization and dose volume histogram prediction functions of RuiPlan were used for automatic planning. Dosimetric differences and planning efficiency between two methods were statistically compared with paired t-test. Results:There were no significant dosimetric differences in the conformity index (CI), homogeneity index (HI) of PTV, V 5Gy of both lungs and D max of the spinal cord between automatic and manual plans (all P>0.05). Compared with those in the manual plans, the V 20Gy and D mean of the left and right lungs generated from automatic plans were reduced by 1.1%, 0.37 Gy and 1.2%, 0.38 Gy (all P<0.05), and the V 30Gy, V 40Gy and D mean of the heart in automatic plans were significantly decreased by 5.1%, 3.0% and 1.41 Gy, respectively (all P<0.05). The labor time, computer working time, and monitor unit (MU) number of automatic plans were significantly decreased by 65.8%, 14.1%, and 17.2%, respectively (all P<0.05). Conclusion:RuiPlan automatic planning scripts can improve the efficiency of esophageal cancer planning by dose prediction and beam angle optimization, providing an alternative for esophageal cancer radiotherapy planning.
