Mai Yan-Hua, Kong Fan-Tu, Yang Yi-Wei, Li Yong-Bao, Song Ting, Zhou Ling-Hong
Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China. E-mail:
Nan Fang Yi Ke Da Xue Xue Bao. 2018 Jun 20;38(6):691-697. doi: 10.3969/j.issn.1673-4254.2018.06.08.
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)%.
在调强放射治疗（IMRT）中，手动反复调整目标以在计划靶区的处方剂量和保护危及器官之间获得最佳权衡是耗时的。在此，我们提出一种新方法来实现自动多目标IMRT优化，该方法将临床偏好量化为约束优先级列表，并根据该列表调整剂量约束以在剂量约束下获得最优解。该方法包含剂量约束的自动调整机制和基于体素加权因子的FMO模型。每次调整剂量约束时，都会启动基于体素加权因子的FMO模型以找到满足当前约束的全局最优解。我们通过比较原始计划和该方法生成的自动优化计划，测试了该方法在6例宫颈癌IMRT中的可行性和有效性。结果表明，在相同的计划靶区覆盖率和均匀性下，自动优化计划在危及器官的剂量 sparing方面比原始计划更好，计划质量更高，并且导致直肠的平均V45从（41.99±13.31）%明显降低到（32.55±22.27）%，膀胱的平均V45从（44.37±4.08）%明显降低到（28.99±15.25）%。
