AbstractObjective To propose an automatic planning platform of the Raystation planning system suitable for multi-disease and multi-plan technique by using the Raystation built-in script function. Methods IronPython and WPF user interface framework were utilized for programming and resolving the differences in the design of different types of plans for different diseases. The program was designed from prescription identification, visual plan parameter input and cost-function setting. The efficiency of automatic planning and manual planning was compared when applied in whole brain irradiation, nasopharyngeal carcinoma, cervical cancer, esophageal cancer and breast cancer, including IMRT and VMAT. The dosimetric parameters of the whole brain irradiation were chosen. Results Physicists were only required to enter and select the necessary parameters to achieve the plan design by using the program. Compared with the five types of diseases, the maximum efficiency of automatic planning was 1.4 times higher than that of manual planning. In the dosimetric evaluation of the whole brain irradiation plan, both manual and automatic planning could meet the clinical needs, and the D2%, CI and HI of the target area did not significantly differ (all P>0.05). The mean D98% of the target area and the Dmax of lens in the manual plan were significantly higher than those in the automatic plan by 0.4% and 7.1%(both P<0.05). Conclusion The developed program has the function of automatic planning system, which can realize the automatic planning of multi-disease and multi-type radiotherapy, significantly improve the efficiency of plan design and has important clinical application value.
Fund:National Natural Science Foundation of China (81773230);Special Fund for Key Research and Development and Promotion of Henan Province in 2018(182102310297)
Corresponding Authors:
Ge Hong, Email:gehong666@126.com
Cite this article:
Lou Zhaoyang,Mao Ronghu,Li Dingjie et al. Study of automatic planning of multi-disease and multi-plan type based on Raystation planning system[J]. Chinese Journal of Radiation Oncology, 2020, 29(11): 968-972.
Lou Zhaoyang,Mao Ronghu,Li Dingjie et al. Study of automatic planning of multi-disease and multi-plan type based on Raystation planning system[J]. Chinese Journal of Radiation Oncology, 2020, 29(11): 968-972.
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