[an error occurred while processing this directive] | [an error occurred while processing this directive]
Evaluation of an algorithm-based automatic treatment planning module for volumetric-modulated arc therapy planning in nasopharyngeal carcinoma
Zhang Daguang, Jiang Shengpeng, Yang Chengwen, Wang Peiguo, Zhang Ximei, Wang Wei
Department of Radiation Oncology,Tianjin Medical University Cancer Institute and Hospital,National Clinical Research Center for Cancer,Key Laboratory of Cancer Prevention and Therapy,Tianjin,Tianjin’s Clinical Research Center for Cancer,Tianjin 300060,China
AbstractObjective To evaluate the performance of progressive optimization algorithm-based Auto-Planning module in automated volumetric-modulated arc therapy (VMAT) planning for nasopharyngeal carcinoma. Methods Thirteen treated VMAT plans of nasopharyngeal carcinoma were re-planed with Auto-Planning module. Only one cycle of automated optimization of the Auto-Planning module was performed for each plan without any manual intervention. The dosimetric parameters of the automated treatment plans were compared with those of the manual plans. Paired t-test was used for statistical analysis. The time required for automated planning using the Auto-Planning module was also measured. Results All plans generated with the Auto-Planning module met the routine dosimetric requirements and were acceptable for clinical use. The homogeneity index of targets was superior in the automated plans than in manual plans (P=0.000).In addition,the automated plans had significantly improved protection for some organs at risk than the manual plans. The mean dose to the left and right parotids were reduced by 7.75 Gy (P=0.000) and 5.79 Gy (P=0.000) in the automated plans,respectively. Furthermore,the V60(0.58% vs. 3.12%,P=0.000) and Dmean(34.11 Gy vs. 40.78 Gy,P=0.000) of the mandible were also significantly lower with Auto-Planning than with manual planning. Conclusions Auto-Planning module can improve the overall quality and consistency of treatment plans,and reduce the workload and time of treatment planning,resulting in substantially enhanced treatment planning efficiency.
Fund:Special Fund for Pharmacy,Laboratory,Radiology and Radiotherapy Departments of Tianjin Medical University Cancer Institute and Hospital (Y1305);The Key Research Project of Tianjin Health Bureau (14KG144)
Corresponding Authors:
Wang Wei,Email:weiwang_2@126.com
Cite this article:
Zhang Daguang,Jiang Shengpeng,Yang Chengwen et al. Evaluation of an algorithm-based automatic treatment planning module for volumetric-modulated arc therapy planning in nasopharyngeal carcinoma[J]. Chinese Journal of Radiation Oncology, 2017, 26(12): 1411-1416.
Zhang Daguang,Jiang Shengpeng,Yang Chengwen et al. Evaluation of an algorithm-based automatic treatment planning module for volumetric-modulated arc therapy planning in nasopharyngeal carcinoma[J]. Chinese Journal of Radiation Oncology, 2017, 26(12): 1411-1416.
[1] Batumalai V,Jameson MG,Forstner DF,et al. How important is dosimetrist experience for intensity modulated radiation therapy? A comparative analysis of a head and neck case[J].Pract Radiat Oncol,2013,3(3):e99-e106.DOI:10.1016/j.prro.2012.06.009. [2] Nelms BE,Robinson G,Markham J,et al. Variation in external beam treatment plan quality:an inter-institutional study of planners and planning systems[J].Pract Radiat Oncol,2012,2(4):296-305.DOI:10.1016/j.prro.2011.11.012. [3] Wu BB,Pang DL,Simari P,et al. Using overlap volume histogram and IMRT plan data to guide and automate VMAT planning:a head-and-neck case study[J].Med Phys,2013,40(2):021714.DOI:10.1118/1.4788671. [4] Yang YD,Ford EC,Wu BB,et al. An overlap-volume-histogram based method for rectal dose prediction and automated treatment planning in the external beam prostate radiotherapy following hydrogel injection[J].Med Phys,2013,40(1):011709.DOI:10.1118/1.4769424. [5] Zhang XD,Li XQ,Quan EM,et al. A methodology for automatic intensity-modulated radiation treatment planning for lung cancer[J].Phys Med Biol,2011,56(13):3873-3893.DOI:10.1088/0031-9155/56/13/009. [6] Chanyavanich V,Das SK,Lee WR,et al. Knowledge-based IMRT treatment planning for prostate cancer[J].Med Phys,2011,38(5):2515-2522.DOI:10.1118/1.3574874. [7] Hazell I,Bzdusek K,Kumar P,et al. Automatic planning of head and neck treatment plans[J].J Appl Clin Med Phys,2016,17(1):272-282.DOI:10.1120/jacmp.v17i1.5901. [8] Krayenbuehl J,Norton I,Studer G,et al. Evaluation of an automated knowledge based treatment planning system for head and neck[J].Radiat Oncol,2015,10(1):226.DOI:10.1186/s13014-015-0533-2. [9] Yuan LL,Ge YR,Lee WR,et al. Quantitative analysis of the factors which affect the interpatient organ-at-risk dose sparing variation in IMRT plans[J].Med Phys,2012,39(11):6868-6878.DOI:10.1118/1.4757927. [10] Appenzoller LM,Michalski JM,Thorstad WL,et al. Predicting dose-volume histograms for organs-at-risk in IMRT planning[J].Med Phys,2012,39(12):7446-7461.DOI:10.1118/1.4761864. [11] Tol JP,Delaney AR,Dahele M,et al. Evaluation of a knowledge-based planning solution for head and neck cancer[J].Int J Radiat Oncol Biol Phys,2015,91(3):612-620.DOI:10.1016/j.ijrobp.2014.11.014.
2017, Vol. 26 
