Application of the RapidPlan knowledge-based treatment planning system for radiation therapy of prostate cancer patients
Keywords:
Knowledge-based planning, Prostate planning, RapidPlan, VMATAbstract
Background: RapidPlan (RP) knowledge-based treatment planning was developed and adopted in volumetric arc modulated radiotherapy (VMAT) planning to improve plan quality and planning efficiency. RP used plan database to train a model for predicting organ-at-risk (OAR) dose-volume-histograms (DVHs) of the new treatment plan. Objectives: The purpose of this study was to develop and evaluate the performance of the RP knowledge-based treatment planning to generate VMAT for definitive radiotherapy of prostate cancer. Materials and methods: Three RP models based on a number of 20, 40, and 60 previously VMAT plans were trained and validated on 10 new prostate cancer patients. Dosimetric parameters of the target volume and organs at risks (OARs) between models and manually optimized method (MO) from experienced planner were compared. The D2%, D95%, D98%, homogeneity index (HI), and conformation number (CN) for planning target volume (PTV), V65Gy, V70Gy, V75Gy for bladder and V50Gy, V60Gy, V65Gy, V70Gy, V75Gy for rectum were collected and analyzed (one-way repeated measures ANOVA, p<0.05). Results: VMAT plans between models and MO showed similar results of D95%, D98% for PTV but a significant higher of D2%, CN, and HI from RP (105.4%-105.7% for D2%, 0.06-0.07 for HI, and 0.9 for CN) when compared with MO (104% for D2%, 0.05 for HI, and 0.8 for CN). For bladder and rectum, all dose-volume parameters of RP were significantly lower than MO (p<0.05), only in RPmodel20 which bladder V75Gy, was similar to MO. Dosimetric analysis for model training based on a different number of VMAT plans showed no statistical difference in plan quality. Conclusion: RP knowledge-based treatment planning in this investigation presented acceptable VMAT plan quality for definitive radiotherapy prostate in only single optimization. Twenty historic plans were found to be an acceptable minimum number of plans for the model training.
References
Nelms BE, Robinson G, Markham J, Velasco K, Boyd S, Narayan S, et al. Variation in external beam treatment plan quality: An interinstitutional study of planners and planning systems. Pract Radiat Oncol 2012; 2:296-305.
Wu B, Ricchetti F, Sanguineti G, Kazhdan M, Simari P, Chuang M, et al. Patient geometry-driven information retrieval for IMRT treatment plan quality control. Med Phys 2009;36:5497-505.
Sutherland K. A Dosimetrist Perspective on the Role of Knowledge Based Planning [online]. 2014 [cited 2017 Jul 12]. Available from: http://pubs.medicaldosimetry.org/pub/ddbcc4e6-f920-82a8-7e3e-e2c37d5eaaec.
Fogliata A, Belosi F, Clivio A, Navarria P, Nicolini G, Scorsetti M, et al. On the pre-clinical validation of a commercial model-based optimisation engine: Application to volumetric modulated arc therapy for patients with lung or prostate cancer. Radiat Oncol 2014; 113: 385-91.
Fogliata A, Nicolini G, Clivio A, Vanetti E, Laksar S, Tozzi A, et al. A broad scope knowledge based model for optimization of VMAT in esophageal cancer: validation and assessment of plan quality among different treatment centers. Radiat Oncol 2015;10:220.
Fogliata A, Wang P-M, Belosi F, Clivio A, Nicolini G, Vanetti E, et al. Assessment of a model based optimization engine for volumetric modulated arc therapy for patients with advanced hepatocellular cancer. Radiat Oncol 2014; 9:236.
Marks LB, Yorke ED, Jackson A, Haken RT, Constine LS, Eisbrruch A, et al. Use of normal tissue complication probability models in the clinic. Int J Radiat Oncol Biol Phys 2010;76: 10-9.
Luca C. RAPIDPLAN: The clinical value and DVH estimation algorithm [online]. 2014 [cited 2015 Jul 16]. Available from: Varian Medical System Source.
Kataria T, Sharma K, Subramani V, Karrthick KP, Bisht SS. Homogeneity Index: An objective tool for assessment of conformal radiation treatments. J Med Phys 2012;37:207-13.
Riet A, Mak A, Moerland M, Elders L, van W. A conformation number to quantify the degree of conformality in brachytherapy and external beam irradiation: Application to the prostate. Int J Radiat Oncol Biol Phys 1997; 37:731-6.
Fogliata A, Reggiori G, Stravato A, Lobefalo F, Franzese C, Franceschini D, et al. RapidPlan head and neck model: the objectives and possible clinical benefit. Radiat Oncol 2017; 12:73.
Wu H, Jiang F, Yue H, Zhang H, Wang K, Zhang Y. Applying a RapidPlan model trained on a technique and orientation to another: a feasibility and dosimetric evaluation. Radiat Oncol 2016; 11:108.
Hussein M, South CP, Barry MA, Adams EJ, Jordan TJ, Stewart AJ, et al. Clinical validation and benchmarking of knowledge-based IMRT and VMAT treatment planning in pelvic anatomy. Radiat Oncol 2016;120:473-9.
Kubo K, Monzen H, Ishii K, Tamura M, Kawamorita R, Sumida I, et al. Dos imetr ic compa r i son of RapidPlan and manually optimized plans in volumetric modulated arc therapy for prostate cancer. Phys Med 2017; 1120-1790:30216–8
Tol JP, Dahele M, Delaney AR, Slotman BJ, Verbakel WF. Can knowledge-based DVH predictions be used for automated, individualized quality assurance of radiotherapy treatment plans. Radiat Oncol 2015;10:234.
Tol JP, Delaney AR, Dahele M, Slotman BJ, Verbakel WFAR. Evaluation of a Knowledge Based Planning Solution for Head and Neck Cancer. Int J Radiat Oncol Biol Phys 2015; 91:612-20.
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