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 南方医科大学学报  2018, Vol. 38Issue (6): 691-697  DOI: 10.3969/j.issn.1673-4254.2018.06.08. 0

引用本文 [复制中英文]

MAI Yanhua, KONG Fantu, YANG Yiwei, LI Yongbao, SONG Ting, ZHOU Linghong. Constraint priority list-based multi-objective optimization for intensity-modulated radiation therapy[J]. Journal of Southern Medical University, 2018, 38(6): 691-697. DOI: 10.3969/j.issn.1673-4254.2018.06.08.

文章历史

1. 南方医科大学生物医学工程学院，广东 广州 510515;
2. 中山大学肿瘤防治中心，广东 广州 510060;
3. 浙江省肿瘤医院放疗科，浙江 杭州 310022

Constraint priority list-based multi-objective optimization for intensity-modulated radiation therapy
MAI Yanhua1, KONG Fantu1, YANG Yiwei3, LI Yongbao2, SONG Ting1, ZHOU Linghong1
1. Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China;
2. Sun Yat-sen University Cancer Center, Guangzhou 510060, China;
3. Department of Radiation Therapy, Zhejiang Provincial Cancer Hospital, Hangzhou 310022, China
Supported by National Natural Science Foundation of China (81571771, 81601577)
Abstract: In intensity-modulated radiation therapy (IMRT), it is time-consuming to repeatedly adjust the objectives manually to obtain the best tradeoff between the prescribed dose of the planning target volume and sparing the organs-at-risk. Here we propose a new method to realize automatic multi-objective IMRT optimization, which quantifies the clinical preferences into the constraint priority list and adjusts the dose constraints based on the list to obtain the optimal solutions under the dose constraints. This method contains automatic adjustment mechanism of the dose constraint and automatic voxel weighting factor-based FMO model. Every time the dose constraint is adjusted, the voxel weighting factor-based FMO model is launched to find a global optimal solution that satisfied the current constraints. We tested the feasibility and effectiveness of this method in 6 cases of cervical cancer with IMRT by comparing the original plan and the automatic optimization plan generated by this method. The results showed that with the same PTV coverage and uniformity, the automatic optimization plan had a better a dose sparing of the organs-at-risk and a better plan quality than the original plan, and resulted in obvious reductions of the average V45 of the rectum from (41.99±13.31)% to (32.55±22.27)% and of the bladder from (44.37±4.08)% to (28.99±15.25)%.
Key words: intensity-modulated radiation therapy    multi-objective optimization constraint priority list    voxel weighting factor    radiotherapy planning

1 材料和方法 1.1 基于约束优先级列表的IMRT自动多目标优化的实现

 图 1 基于约束优先级列表的IMRT自动多目标优化流程图 Figure 1 Constraint priority list-dependent IMRT automatic multi-objective optimization framework.
1.2 约束优先级列表的建立

1.3 剂量约束的调整

1.4 基于体素权重因子的FMO模型

 $\begin{array}{l} \min f\left( x \right) = \sum\limits_{v \in V} {{\xi _v}{{\left( {{D_v}x - d_v^p} \right)}^T}} {{\tilde \eta }_v}\left( {{D_v}x - d_v^p} \right) + s{\left( {Mx} \right)^T}\left( {Mx} \right)\\ \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;s.t.\;0 \le x \le C, \\ \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;{g_i}\left( x \right) \le 0, i = 1, 2, \cdots , m. \end{array}$ (1)

1.5 体素权重因子的调整 1.5.1 调整体素的选择

1.5.2 体素权重值的更新

1.6 方法验证

2 结果 2.1 DVH图比较

 图 2 6例宫颈癌病例的DVH比较图 Figure 2 DVH comparison in the 6 cases of cervical cancer. Solid line: Optimized plan; Dashed line: Original plan.
2.2 剂量约束点比较

 图 3 6例宫颈癌病例的剂量约束点平均结果比较 Figure 3 Comparison of endpoints in the 6 cases of cervical cancer. A: Hard constraints; B: Soft constraints.

3 讨论

DVH比较结果显示，优化计划PTV的DVH曲线更加陡峭，OAR的DVH曲线大部分都在原始计划下方，说明优化计划PTV覆盖率和剂量均匀性提升，同时OAR接受剂量下降，更好实现放疗计划优化的目的。剂量约束点的平均对比结果从客观数据的角度说明了，在PTV覆盖率和剂量均匀性提升并且满足所有硬约束的同时，OAR剂量约束点具体数值下降，计划质量提升，这与DVH比较得到的结论是一致的；配对P检验比较结果中，骨骼的平均V10V20均低于原始计划，且具有统计学差异(P < 0.05)，说明骨骼受照剂量降低是明显的；虽然直肠、膀胱、股骨头平均最大剂量Dmax高于原始计划，但是其满足硬约束，且两者之间不具有统计学差异(P > 0.05)，说明直肠、膀胱、股骨头最大剂量的增加是不明显的，所以其增加是可以接受的。其余剂量约束项不具有统计学差异(P > 0.05)，说明PTV覆盖率、剂量均匀性和其剂量约束的质量是相当的。此外，DVH比较结果中，优化计划少数OAR的DVH曲线与原始计划之间存在交叉，但交叉出现的区域极少存在临床规范关注的剂量约束点，可能是由于对规范中的剂量约束点更多关注。