Department of Radiotherapy,Peking University Cancer Hospital and Institute,Key Laboratory of Carcinogenesis and Translational Research,Ministry of Education,Beijing 100142,China
Abstract：Objective To evaluate the feasibility and dosimetric features of a volume modulated arc therapy (VMAT)-configured model in knowledge-based optimization of fixed-field dynamic intensity-modulated radiotherapy (IMRT) plans based on the Varian RapidPlan system. Methods ① A dose-volume histogram prediction model was trained with 81 qualified preoperative VMAT plans for rectal cancer and then statistically verified. ② For clinically approved 10 IMRT plans with the same dose prescription, the above model was used to automatically generate new optimization parameters and dynamic multileaf collimator (MLC) sequences with field geometry and beam energy unchanged. ③ In order to rule out the disparities between different versions, a single algorithm was used to calculate the absolute doses of the original and new plans. ④ Statistical analyses were performed on dosimetric parameters after comparable target dose coverage was achieved in the two plans by appropriate normalization. Results On the basis of similar target dose homogeneity and coverage, RapidPlan significantly reduced the doses to the urinary bladder (D50% by 9.01 Gy, P=0.000;Dmean by 8.08 Gy, P=0.005) and the femoral head (D50% by 4.20 Gy, P=0.000;Dmean by 3.84 Gy, P=0.005) but significantly elevated the mean total number of monitor units (1211±99 vs. 771±79, P=0.000) and the number of fields with multiple MLC carriage groups. The knowledge-based semi-automated optimization caused a significantly larger number of high-dose hotspots but a similar D2%(52.54 vs. 52.71 Gy, P=0.239). Conclusions The VMAT model can be used for the knowledge-based semi-automated optimization of IMRT plans to enhance the efficiency and OAR protection. However, the resulting high-dose hotspots need further manual intervention.
Zhang Yibao,Jiang Fan,Yue Haizhen et al. Knowledge-based semi-automated optimization of intensity-modulated radiotherapy plans using a volume modulated arc therapy-configured model[J]. Chinese Journal of Radiation Oncology, 2017, 26(2): 178-181.
[1] 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. [2] Zarepisheh M,Long T,Li N,et al. A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning[J].Med Phys,2014,41(6):061711.DOI:10.1118/1.4875700. [3] Bolan C.Expediting the treatment planning process[J].Appl Radiat Oncol,2013(1):19-23. [4] oore KL,Brame RS,Low DA,et al. Experience-based quality control of clinical intensity-modulated radiotherapy planning[J].Int J Radiat Oncol Biol Phys,2011,81(2):545-551.DOI:10.1016/j.ijrobp.2010.11.030. [5] Good D,Lo J,Lee WR,et al. A knowledge-based approach to improving and homogenizing intensity modulated radiation therapy planning quality among treatment centers:an example application to prostate cancer planning[J].Int J Radiat Oncol Biol Phys,2013,87(1):176-181.DOI:10.1016/j.ijrobp.2013.03.015. [6] 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. [7] 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. [8] Zhu XF,Ge YR,Li TR,et al. A planning quality evaluation tool for prostate adaptive IMRT based on machine learning[J].Med Phys,2011,38(2):719-726.DOI:10.1118/1.3539749. [9] 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. [10] Lian J,Yuan LL,Ge YR,et al. Modeling the dosimetry of organ-at-risk in head and neck IMRT planning:an intertechnique and interinstitutional study[J].Med Phys,2013,40(12):121704.DOI:10.1118/1.4828788. [11] Wu BB,McNutt T,Zahurak M,et al. Fully automated simultaneous integrated boosted–intensity modulated radiation therapy treatment planning is feasible for head-and-neck cancer:a prospective clinical study[J].Int J Radiat Oncol Biol Phys,2012,84(5):e647-e653.DOI:10.1016/j.ijrobp.2012.06.047. [12] 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. [13] Fogliata A,Belosi F,Clivio A,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[J].Radiothera Oncol,2014,113(3):385-391.DOI:10.1016/j.radonc.2014.11.009. [14] Fogliata A,Wang PM,Belosi F,et al. Assessment of a model based optimization engine for volumetric modulated arc therapy for patients with advanced hepatocellular cancer[J].Radiat Oncol,2014,9:236.DOI:10.1186/s13014-014-0236-0. [15] Nwankwo O,Mekdash H,Sihono DSK,et al. Knowledge-based radiation therapy (KBRT) treatment planning versus planning by experts:validation of a KBRT algorithm for prostate cancer treatment planning[J].Radiat Oncol,2015,10(1):111.DOI:10.1186/s13014-015-0416-6.
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