AbstractObjective 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, V5Gy of both lungs and Dmax of the spinal cord between automatic and manual plans (all P>0.05).compared with those in the manual plans, the V20Gy and Dmean of the left and right lungs generated from automatic plans were reduced by 1.1%, 0.37Gy and 1.2%, 0.38Gy (all P<0.05), and the V30Gy, V40Gy and Dmean of the heart in automatic plans were significantly decreased by 5.1%, 3.0% and 1.41Gy, 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.
Fund:Joint Construction Project of Henan Medical Science and Technology Research Program in 2019(LHGJ20190661);National Natural Science Foundation of China (81773230)
Corresponding Authors:
Ge Hong, Email:gehong666@126.com
Cite this article:
Lou Zhaoyang,Lei Hongchang,Mao Ronghu et al. A study of automatic planning for esophageal cancer with intensity-modulated radiotherapy based on dose prediction and beam angle optimization[J]. Chinese Journal of Radiation Oncology, 2021, 30(12): 1275-1279.
Lou Zhaoyang,Lei Hongchang,Mao Ronghu et al. A study of automatic planning for esophageal cancer with intensity-modulated radiotherapy based on dose prediction and beam angle optimization[J]. Chinese Journal of Radiation Oncology, 2021, 30(12): 1275-1279.
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