两种不同优化算法在胸部肿瘤调强放射治疗计划中的对比分析
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摘要
目的分析两种不同优化算法在胸部肿瘤调强放射治疗计划中的差异。方法选取近年来于我院进行治疗的40例中段食管癌患者作为研究对象,将所有患者采用子野优化算法(DMPO)设计放射治疗计划并计算等剂量曲线及DVH图;然后保持原定归一点、射野方向、射野外放范围,改用蒙特卡洛(Monte Carlo)算法对患者放射治疗计划进行重新计算,获得新的等剂量曲线及DVH图。分析两种优化算法的等剂量曲线剂量分布、CI与HI、计划执行效率的差异。结果靶区等剂量曲线剂量分布项目数据对比,DMPO算法与Monte Carlo算法无明显差异(P>0.05);危及器官项目数据对比,MCO算法全肺V_(10)、V_(20)、V_(30)、D_(mean-1),心脏V_(30)、V_(40)、D_(mean-h),脊髓D_(1cm3-s)、D_(mean-s)等显著低于DMPO算法(P<0.05); DMPO算法计算时间明显长于MCO算法(P<0.05)。结论MCO算法在强放射治疗计划中的应用可减少患者多器官并发症的发生,并节约时间,提高工作效率,有临床推广应用价值。
Objective To analyze the differences between two different optimization algorithms in the treatment of chest tumor intensity modulated radiation therapy. Methods Forty patients with esophageal cancer who had been treated treated in our hospital were selected as subjects. All patients were designed with the subfield optimization algorithm(DMPO) and calculated the isodose curve and DVH graph. Then maintain the original return point, field direction, and field range, and use the Monte Carlo algorithm to recalculate the patient's radiotherapy plan to obtain a new isodose curve and DVH graph. The difference of the dose-distribution,Cl and HI,and planned execution efficiency of the two optimization algorithms were analyzed. Results There was no significant difference between the DMPO algorithm and Monte Carlo algorithm(P>0.05). Comparison of organ hazard data, MCO algorithm whole lung V_(10), V_(20),V_(30), D_(mean-1),heart V_(30), V_(40), D_(mean-h), spinal cord D_(1 cm3-s), D_(mean-s), etc. are significantly lower than DMPO algorithm(P<0.05);the DMPO algorithm is significantly longer than the MCO algorithm(P<0.05). Conclusion The application of MCO algorithm in the intensive radiotherapy program can reduce the occurrence of multiorgan complications, save time,improve work efficiency, and have clinical application value.
Objective To analyze the differences between two different optimization algorithms in the treatment of chest tumor intensity modulated radiation therapy. Methods Forty patients with esophageal cancer who had been treated treated in our hospital were selected as subjects. All patients were designed with the subfield optimization algorithm(DMPO) and calculated the isodose curve and DVH graph. Then maintain the original return point, field direction, and field range, and use the Monte Carlo algorithm to recalculate the patient's radiotherapy plan to obtain a new isodose curve and DVH graph. The difference of the dose-distribution,Cl and HI,and planned execution efficiency of the two optimization algorithms were analyzed. Results There was no significant difference between the DMPO algorithm and Monte Carlo algorithm(P>0.05). Comparison of organ hazard data, MCO algorithm whole lung V_(10), V_(20),V_(30), D_(mean-1),heart V_(30), V_(40), D_(mean-h), spinal cord D_(1 cm3-s), D_(mean-s), etc. are significantly lower than DMPO algorithm(P<0.05);the DMPO algorithm is significantly longer than the MCO algorithm(P<0.05). Conclusion The application of MCO algorithm in the intensive radiotherapy program can reduce the occurrence of multiorgan complications, save time,improve work efficiency, and have clinical application value.
引文
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[7]吴建亭,狄慧,施春明.两种算法在宫颈癌术后调强放疗中的剂量学比较研究[J].肿瘤预防与治疗,2017,30(1):39-42.
[8]柏晗,夏耀雄,李政良.基于四维CT下胸部肿瘤调强计划中心脏和食道的剂量评估[J].中国医学物理学杂志,2016,33(3):293-296.
[9]周锡垒,于长华,潘鹏.胸部上中段食管癌术后不均一靶区照射物理指标及临床反应观察[J].现代肿瘤医学,2016,24(5):736-740.
[10]赖霄晶,谷庆,杨双燕.食管癌螺旋断层放疗及三维适形调强放疗计划剂量学研究[J].中华肿瘤防治杂志,2017,24(10):696-702.
[2]陈旎,张九堂,刘义保,等.XVMC算法和PB算法在非小细胞肺癌调强放疗中的剂量学比较[J].肿瘤学杂志,2016,22(3):188-193.
[3]周正东,舒华忠,於文雪.一种调强放射治疗逆向计划多目标混合优化方法研究[J].中国图象图形学报,2018,10(7):905-909.
[4]杨晓梅,储开岳,金建华,等.胸部定位参考线配合红外定位系统在鼻咽癌放疗中的应用[J].中国医学物理学杂志,2017,34(2):147-150.
[5]邵凯南,杜锋磊,李剑龙.Ray Arc在胸部旋转调强放射治疗计划设计中的应用[J].中国医学物理学杂志,2017,34(2):131-138.
[6]要雪品,翟福山,王建廷.肝脏肿瘤不同调强放疗计划的剂量学对比[J].现代肿瘤医学,2016,24(22):3600-3602.
[7]吴建亭,狄慧,施春明.两种算法在宫颈癌术后调强放疗中的剂量学比较研究[J].肿瘤预防与治疗,2017,30(1):39-42.
[8]柏晗,夏耀雄,李政良.基于四维CT下胸部肿瘤调强计划中心脏和食道的剂量评估[J].中国医学物理学杂志,2016,33(3):293-296.
[9]周锡垒,于长华,潘鹏.胸部上中段食管癌术后不均一靶区照射物理指标及临床反应观察[J].现代肿瘤医学,2016,24(5):736-740.
[10]赖霄晶,谷庆,杨双燕.食管癌螺旋断层放疗及三维适形调强放疗计划剂量学研究[J].中华肿瘤防治杂志,2017,24(10):696-702.