[an error occurred while processing this directive] | [an error occurred while processing this directive]
A study of automatic treatment planning based on kernel density estimation
Fan Jiawei,Wang Jiazhou,Hu Weigang
Department of Radiation Oncology,Fudan University Shanghai Cancer Center;Department of Oncology,Shanghai Medical College,Fudan University,Shanghai 200032,China
Abstract Objective To develop an automatic algorithm to predict the dose-volume histogram (DVH) and implement it in clinical practice. Methods Based on the prior information in the existing plan, such as dosimetric results of organs at risk (OARs) and OAR-target spatial relationship, a two-dimensional kernel density estimation was implemented to predict the DVH of OARs. The predicted DVH curves were converted into objective functions that would be implemented in the Pinnacle treatment planning system. Comparisons between predicted and actual values and between Auto-plan and manual planning were made by paired t test. Results We applied this algorithm to 10 rectal cancer patients, 10 breast cancer patients, and 10 nasopharyngeal carcinoma patients. The predicted DVH of OARs showed that the deviation between the actual and predicted values at important clinical dose points were within 5%(P>0.05). The re-planning for the 10 breast cancer patients using Auto-plan showed that the heart dose was significantly reduced and the target coverage was increased, which was consistent with the predicted results. Conclusions The method proposed in this study allows for accurat DVH prediction, and, combined with Auto-plan, can be used to generate clinically accepted treatment plans.
Corresponding Authors:
Hu Weigang,Email:jackhuwg@gmail.com
Cite this article:
Fan Jiawei,Wang Jiazhou,Hu Weigang. A study of automatic treatment planning based on kernel density estimation[J]. Chinese Journal of Radiation Oncology, 2017, 26(6): 661-666.
Fan Jiawei,Wang Jiazhou,Hu Weigang. A study of automatic treatment planning based on kernel density estimation[J]. Chinese Journal of Radiation Oncology, 2017, 26(6): 661-666.
[1] 任信信,戴建荣,张岳,等.基于专家库的γ射线立体定向放疗计划优化方法[J].中国医学物理学杂志,2015,1(3):12-17.DOI:10.3969/j.issn.1005-202X.2015.03.001. Ren XX,Dai JR,Zhang Y,et al. Optimization method of gamma ray stereotactic radiotherapy based on expert database[J].Chin J Med Phys,2015,1(3):12-17.DOI:10.3969/j.issn.1005-202X.2015.03.001. [2] 张岳,戴建荣.基于混合整数线性规划的γ射线立体定向放疗的计划优化方法[J].国际放射医学核医学杂志,2009,33(1):58-61.DOI:10.1186/1748-717X-6-175. Zhang Y,Dai JR.Plan optimization of gamma ray stereotactic radiotherapy based on mixed integer linear programming[J].Int J Nucl Med,2009,33(1):58-61.DOI:10.1186/1748-717X-6-175. [3] Hu WG,Wang JZ,Li G,et al. Investigation of plan quality between RapidArc and IMRT for gastric cancer based on a novel beam angle and multicriteria optimization technique[J].Radiother Oncol,2014,111(3):144-147.DOI:10.1016/j.radonc.2014.01.024. [4] Yuan L,Wu QJ,Yin FF,et al. Incorporating single-side sparing in models for predicting parotid dose sparing in head and neck IMRT[J].Med Phys,2014,41(02):17-28.DOI:10.1118/1.4862075. [5] Wu BB,Ricchetti F,Sanguineti G,et al. Patient geometry-driven information retrieval for IMRT treatment plan quality control[J].Med Phys,2009,36(12):5497-5505.DOI:10.1118/1.3253464. [6] Wu BB,Pang D,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):17-24.DOI:10.1118/1.4788671. [7] 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,2015,91(2):612-620.DOI:10.1016/j.ijrobp.2014.11.014. [8] Satomi S,Moore L.Knowledge-based prediction of three-dimensional dose distributions for external beam radiotherapy[J].Med Phys,2016,43(6):378.DOI:10.1118/1.4938583. [9] Zhu XF,Ge Y,Li T,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. [10] Lian J,Yuan L,Ge Y,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(3):17-24.DOI:10.1118/1.4828788. [11] Moore KL,Brame RS,Low DA,et al. Experience-based quality control of clinical intensity-modulated radiotherapy planning[J].Int J Radiat Oncol,2011,81(8):545-551.DOI:10.1016/j.ijrobp.2010.11.030. [12] Holdsworth C,Kim M,Liao J,et al. The use of a multi-objective evolutionary algorithm to increase flexibility in the search for better IMRT plans[J].Med Phys,2012,39(2):2261-2274.DOI:10.1118/1.3697535. [13] Craft D,Bortfeld T.How many plans are needed in an IMRT multi-objective plan database[J].Phys Med Biol,53(27):85-96.DOI:10.1088/0031-9155/53/11/002. [14] Halabi T,Craft D,Bortfeld T.Dose-volume objectives in multi-criteria optimization[J].Phys Med Biol,2006,51(38):9-18.DOI:10.1088/0031-9155/51/15/014. [15] Wang JZ,Xian CJ,Zhao KK,et al. Patient feature based dosimetric Pareto front prediction in esophageal cancer radiotherapy[J].Med Phys,2015,42(2):5-10.DOI:10.1118/1.4906252. [16] Zinchenko Y.A moment-based approach for DVH-guided radiotherapy treatment plan optimization[J].Phys Med Biol,2013,58(18):69-78.DOI:10.1088/0031-9155/58/6/1869. [17] Johanna SM.Dose-volume histogram prediction using density estimation[J].Phys Med Biol,2015,60(69):23-36.DOI:10.1088/0031-9155/60/17/6923. [18] Ritter F,Boskamp T,Homeyer A,et al. Medical Image Analysis:A visual approach[J].IEEE Pulse,2011,2(6):60-70.DOI:10.1109/TVCG.2009.58 [19] Heckel F,Schwier M,Peitgen HO.Object Oriented Application Development with MeVisLab and Python[J].Lect Not Informatics,2009,154(13):38-51.DOI:10.1007/s00234-015-1607. [20] Rene B,Fons R.ROOT-An object oriented data analysis framework[J].Nucl Inst Meth Phys Res A,1997,389(8):81-86.DOI:10.1016/S0168-9002.
2017, Vol. 26 
