1(Electronic Engineering Department, Taiyuan Institute of Technology, Taiyuan 030008, China) 2( Institute of Information and Communication Engineering, North University of China, Taiyuan 030051, China)
Abstract：To solve the problem of time-consuming and low efficiency that exist in the process of manual inverse planning, a strategy adjusting weighting factor for sub-objective function of physical criteria was constructed based on the definite integral theory, and an automatic iterative adjustment method of weighting factors was proposed in this paper. In the automatic method, the weighting factors are automatically and iteratively adjusted first based on proposed penalty strategies. Then, plan evaluation was performed to determine whether the obtained plan was acceptable. If not, a higher penalty was assigned to the unsatisfied objective by multiplying it by a compensation coefficient. The optimization processes were performed alternately until an acceptable plan was obtained or the maximum number of iteration was reached. The effectiveness of the method was verified on 10 prostate cancer cases and compared with the manual planning from the perspective of dosimetry and biology. Experimental results showed that, in terms of DVH curves and dose statistics, the mean dose, V65, V70, normal tissue complications probability and generalized equivalent uniform dose of bladder were reduced by 0.53Gy, 4.6%, 3.33%, 0.37% and 0.22Gy, respectively, under the premise that the dose coverage characteristics of target area were similar. And the maximum dose of rectum, V50 and V65 decreased by 0.54Gy, 0.66% and 1.64%, respectively. There was no significant difference in other indexes (P>0.05) except V75 of bladder (P<0.05); it took 1~3 minutes to produce an acceptable plan applying our proposed automatic optimization method of weighting factors, while for the manual trial-and-error method, it needed professional physicians to take 1~3 hours to obtained an acceptable plan. In conclusion, the automatic optimization method of radiotherapy weighting factors based on definite integral improved the efficiency of radiotherapy and generated satisfactory radiotherapy plans.
郭彩萍, 张俊生, 张晓娟. 基于定积分的放疗权重因子自动优化方法研究[J]. 中国生物医学工程学报, 2022, 41(3): 320-327.
Guo Caiping, Zhang Junsheng, Zhang Xiaojuan. Research on Automatic Optimization of Weighting Factors in Radiotherapy Based on Definite Integral. Chinese Journal of Biomedical Engineering, 2022, 41(3): 320-327.
[1] Cotrutz C, Lahanas M, Kappas C, et al. A multiobjective gradient-based dose optimization algorithm for external beam conformal radiotherapy [J]. Phys Med Biol, 2001, 46(8): 2161-2175. [2] Das S. A role for biological optimization within the current treatment planning paradigm. Med Phys, 2009, 36(10): 4672-4682. [3] 张鹏程.精确放射治疗剂量计算及方案优化研究[D].南京:东南大学,2014. [4] Zarepisheh M, Uribe-Sanchez AF, Li Nan, et al. A multicriteria framework with voxel-dependent parameters for radiotherapy treatment plan optimization [J].Med Phys, 2014, 41 (4): 041705-041705. [5] Bokrantz R, Miettinen K. Projections onto the Pareto surface in multicriteria radiation therapy optimization [J]. Med Phys, 2015, 42(10): 5862-5870. [6] Xing Lei, Li JG, Donaldson S, et al. Optimization of importance factors in inverse planning [J]. Phys Med Biol, 1999, 44(10): 2525-2536. [7] 李楠.放射治疗计划的自动优化及再优化关键技术研究[D].广州:南方医科大学,2014. [8] Chanyavanich V, Das SK, Lee WR, et al. Knowledge-based IMRT treatment planning for prostate cancer [J].Med Phys, 2011,38(5): 2515-2522. [9] 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. [10] Li Nan, Zarepisheh M, Uribe-Sanchez AF, et al.Automatic treatment plan re-optimization for adaptive radiotherapy guided with the initial plan DVHs[J].Phys Med Biol,2013,58(24):8725-8738. [11] Zarepisheh M, Long T, Li Nan, et al. A DVH-guided optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning [J].Med Phys, 2012, 41(6): 061711–061711. [12] 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. [13] Moore KL, Brame RS, Low DA. Quantitative metrics for assessing plan quality [J].Semin Radiat Oncol, 2012, 22(1): 62-69. [14] Zhu Xiaofeng, Ge Yaorong, Li Taoran, et al. A planning quality evaluation tool for prostate adaptive IMRT based on machine learning[J]. Med Phys, 2011, 38(2):719-726. [15] Lee T, Hammad M, Timothy CY, et al. Predicting objective function weights from patient anatomy in prostate IMRT treatment planning [J]. Med Phys, 2013, 40(2): 121706. [16] Zhang HH, Meyer RR, Shi L. The minimum knowledge base for predicting organ-at-risk dose-volume levels and plan-related complications in IMRT planning [J]. Phys Med Biol, 2010, 55(7): 1935-1947. [17] Appenzoller LM, Michaski JM, Thorstad WL, et al.Predicting dose-volume histograms for organs-at-risk in IMRT planning [J]. Med Phys, 2012, 39(12): 7446-7461. [18] Dias J, Rocha H, Ventura T, et al. Automated fluence map optimization based on fuzzy inference systems [J]. Med Phys, 2016, 43(3): 1083-1095. [19] 郭彩萍, 舒华忠, 桂志国, 等. 基于混合准则的IMRT计划优化[J]. 中国生物医学工程学报, 2016, 35(6):712-718. [20] Wang Xiaochun, Zhang Xiaodong, Dong Lei, et al. Effectiveness of noncoplanar IMRT planning using a parallelized multiresolution beam angle optimization method for paranasal sinus carcinoma [J]. Int J Radiat Oncol Biol Phys, 2005, 63(2): 594-601. [21] Marks LB, Yorke ED, Jackson A, et al. Use of normal tissuecomplication probability models in the clinic [J]. Int J Radiat Oncol Biol Phys, 2010,76(S3):10-19. [22] 胡逸民主编.肿瘤放射物理学[M].北京:原子能出版社.1999:331. [23] Guo Caiping,Zhang Pengcheng,Gui Zhiguo,et al. Prescription value-based automatic optimization of importance factors in inverse planning [J]. Technology in Cancer Research &Treatment, 2019, 18:1-13. [24] Deasy JO. Multiple local minima in radiotherapy optimization problems with dose-volume constraints [J]. Medical Physics, 1997, 24(7): 1157-1161. [25] Webb S. Optimisation of conformal radiotherapy dose distribution by simulated annealing[J]. Phys Med Biol, 1989, 34(10):1349-1370. [26] Chuan Wu, Gustavo HO, Robert J, et al. Treatment plan modification using voxel-based weighting factors/dose prescription[J]. Phys Med Biol, 2003, 48(15): 2479-2491. [27] Zhang Xiaodong, Wang Xiaochun, Dong Lei, et al. A sensitivity-guided algorithm for automated determination of IMRT objective function parameters [J].Med Phys, 2006, 33(8): 2935-2944 . [28] 李国丽, 吴宜灿, 宋钢,等. 目标函数设置对放疗逆向计划多目标优化过程的影响[J]. 原子核物理评论, 2006,23(2):233-236. [29] 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.
