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利用多目标优化技术的KBP模型精炼方法研究
蔡马凡1, 左国平1, 杨振2, 曹瑛2, 张子健2, 胡永梅2, 杨晓喻2
1南华大学核科学技术学院,衡阳 421001; 2中南大学湘雅医院肿瘤科,长沙 410008
Research on KBP model refining method using multi‐criterion optimization technology
Cai Mafan1, Zuo Guoping1, Yang Zhen2, Cao Ying2, Zhang Zijian2, Hu Yongmei2, Yang Xiaoyu2
1School of Nuclear Science and Technology, University of South China, Hengyang 421001, China; 2Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
Abstract:Objective Utilizing multi‐criterion optimization (MCO) technology to improve plan design quality based on knowledge‐based planning (KBP) model. Methods Fifty‐five patients with nasopharyngeal carcinoma (NPC) who had completed radiotherapy were selected, and fixed‐field intensity‐modulated radiotherapy (IMRT) technology was used in each case. Among them, 40 cases were randomly selected as training set 1. Then, IMRT plans in training set 1 were preprocessed by MCO technology to construct a new training set 2. With the initial training set 1 and the processed training set 2 as training samples, the traditional KBP model and the MCO‐KBP model refined by MCO technology were trained, respectively. Among the remaining 15 cases, 5 cases were randomly selected as the validation set, and the remaining 10 cases were used as the test set. After verification, the test set was used to statistically analyze the plan quality of the initial manual plan and the automatic plan generated by the traditional KBP model and the MCO‐KBP model. Results The target dose (D95%) of plans generated by the traditional KBP model and the MCO‐KBP model met the clinical requirements. Conformity index (CI) and homogeneity index (HI) were almost the same (P>0.05), and the doses of organ at risk (OAR) of the automatic plans generated by the MCO‐KBP model were lower than those of the traditional KBP model. For example, compared with the traditional KBP model, the average Dmax of the brainstem in the automatic plans generated by the MCO‐KBP model was lower by 2.13 Gy, the average Dmean of the left parotid gland was lower by 1.39 Gy, the average Dmean of the right parotid gland was lower by 1.59 Gy, and the average Dmax of the left optic nerve was lower by 1.42 Gy, the average Dmax of the right optic nerve was lower by 1.16 Gy, and the average Dmax of the pituitary gland was lower by 1.88 Gy. All of the above‐mentioned dosimetry indexes were statistically significant. Conclusion Compared with the traditional KBP model, the IMRT plans designed by the refined MCO‐KBP model have obvious advantages in the protection of OAR, which proves the feasibility of utilizing MCO technology to improve the plan design quality of the KBP model.
Cai Mafan,Zuo Guoping,Yang Zhen et al. Research on KBP model refining method using multi‐criterion optimization technology[J]. Chinese Journal of Radiation Oncology, 2022, 31(9): 811-816.
[1] Moore 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. [2] 高磊,吕庆文,李树祥,等.一种求调强放疗中最佳笔射束强度分布的改进方法[J].北京生物医学工程, 1999(03):40‐42. DOI: 10.3969/j.issn.1002-3208.1999.03.009 Gao L, Lyu QW, Li SX, et al. An improved method for optimal pen beam intensity distribution in intensity modulated radiotherapy[J]. Beijing J Biomed Engineer, 1999(03): 40‐42. DOI: 10.3969/j.issn.1002-3208.1999.03.009 [3] 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. [4] Delaney AR, Tol JP, Dahele M, et al. Effect of dosimetric outliers on the performance of a commercial knowledge‐based planning solution[J]. Int J Radiat Oncol Biol Phys, 2016, 94(3):469‐477. DOI: 10.1016/j.ijrobp.2015.11.011. [5] Nwankwo O, Mekdash H, Sihono DS, 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, 10:111. DOI: 10.1186/s13014‐015‐0416‐6. [6] Boutilier JJ, Craig T, Sharpe MB, et al. Sample size requirements for knowledge‐based treatment planning[J]. Med Phys, 2016, 43(3):1212‐1221. DOI: 10.1118/1.4941363. [7] Wu B, Ricchetti F, Sanguineti G, et al. Data‐driven approach to generating achievable dose‐volume histogram objectives in intensity‐modulated radiotherapy planning[J]. Int J Radiat Oncol Biol Phys, 2011, 79(4):1241‐1247. DOI: 10.1016/j.ijrobp.2010.05.026. [8] Yuan L, Ge Y, 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. [9] 毕苏艳, 代智涛,丁真,等.基于经验计划的VMAT和IMRT模型预测前列腺IMRT剂量分布的对比研究[J].中华放射肿瘤学杂志, 2021, 30(02):164‐169. DOI: 10.3760/cma.j.cn113030-20190514-00179. Bi SY, Dai ZT, Ding Z, et al. Comparative study of VMAT and IMRT models based on empirical planning to predict prostate IMRT dose distribution[J]. Chin J Radiat Oncol, 2021, 30(02): 164‐169. DOI: 10.3760/cma.j.cn113030-20190514-00179. [10] Wu B, 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. [11] 王翰宇, 邱小平, 杨振, 等. RapidPlan精炼模型方法在宫颈癌中的应用[J]. 中国医学物理学杂志, 2017, 34(2): 157‐160,165. DOI: 10.3969/j.issn.1005‐202X.2017.02.009. Hanyu W, Xiaoping Q, Zhen Y, et al.A new approach to model refinement in RapidPlan for predicting intensity‐modulated radiotherapy plans for cervical cancer[J].Chin J Med Phys, 2017,34(2):157‐160,165. DOI: 10.3969/j.issn.1005‐202X.2017.02.009. [12] Craft DL, Hong TS, Shih HA, et al. Improved planning time and plan quality through multicriteria optimization for intensity‐modulated radiotherapy[J]. Int J Radiat Oncol Biol Phys, 2012, 82(1):e83‐90. DOI: 10.1016/j.ijrobp.2010.12.007. [13] Wala J, Craft D, Paly J, et al. Maximizing dosimetric benefits of IMRT in the treatment of localized prostate cancer through multicriteria optimization planning[J]. Med Dosim, 2013, 38(3):298‐303. DOI: 10.1016/j.meddos.2013.02.012. [14] 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. [15] 邱健健,赵俊,彭佳元,等.基于容积调强弧形技术模型的全自动逆向优化方案的设计及应用[J].中华放射医学与防护杂志, 2013. 33(05):497‐500. DOI:10.3760/cma.j.issn.0254-5098.2013.05.010. Qiu JJ, Zhao J, Peng JJ, et al. Design and application of automatic reverse optimization scheme based on volumetric intensity modded arc technology model[J]. Chin J Radiat Med Protect, 2013. 33(05): 497‐500. DOI:10.3760/cma.j.issn.0254-5098.2013.05.010. [16] Zhu X, 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. [17] Wall P, Carver RL, Fontenot JD.An improved distance‐to‐dose correlation for predicting bladder and rectum dose‐volumes in knowledge‐based VMAT planning for prostate cancer[J]. Phys Med Biol, 2018, 63(1):015035. DOI: 10.1088/1361‐6560/aa9a30. [18] Zhuang Y, Han J, Chen L, et al. Dose‐volume histogram prediction in volumetric modulated arc therapy for nasopharyngeal carcinomas based on uniform‐intensity radiation with equal angle intervals[J]. Phys Med Biol, 2019, 64(23):23NT03. DOI: 10.1088/1361‐6560/ab5433. [19] 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, 10:226. DOI: 10.1186/s13014‐015‐0533‐2. [20] Jiao SX, Chen LX, Zhu JH, et al. Prediction of dose‐volume histograms in nasopharyngeal cancer IMRT using geometric and dosimetric information[J]. Phys Med Biol, 2019, 64(23):23NT04. DOI: 10.1088/1361‐6560/ab50eb. [21] 范嘉伟,王佳舟,胡伟刚,等.基于核密度估计的自动计划研究[J].中华放射肿瘤学杂志, 2017, 26(06):661‐666. DOI:10.3760/cma.j.issn.1004-4221.2017.06.012. Fan JW, Wang JZ, Hu WG, et al. Automatic planning based on kernel density Estimation[J]. Chin J Radiat Oncol, 2017, 26(06): 661‐666. DOI:10.3760/cma.j.issn.1004-4221.2017. 06.012.