Liu Eva Sau Fan, Wu Vincent Wing Cheung, Harris Benjamin, Lehman Margot, Pryor David, Chan Lawrence Wing Chi
Department of Radiation Oncology, Princess Alexandra Hospital, Brisbane, Australia; Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong.
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong.
Med Dosim. 2017;42(2):79-84. doi: 10.1016/j.meddos.2017.01.001. Epub 2017 Mar 18.
Lengthy time consumed in traditional manual plan optimization can limit the use of step-and-shoot intensity-modulated radiotherapy/volumetric-modulated radiotherapy (S&S IMRT/VMAT). A vector model base, retrieving similar radiotherapy cases, was developed with respect to the structural and physiologic features extracted from the Digital Imaging and Communications in Medicine (DICOM) files. Planning parameters were retrieved from the selected similar reference case and applied to the test case to bypass the gradual adjustment of planning parameters. Therefore, the planning time spent on the traditional trial-and-error manual optimization approach in the beginning of optimization could be reduced. Each S&S IMRT/VMAT prostate reference database comprised 100 previously treated cases. Prostate cases were replanned with both traditional optimization and vector-model-supported optimization based on the oncologists' clinical dose prescriptions. A total of 360 plans, which consisted of 30 cases of S&S IMRT, 30 cases of 1-arc VMAT, and 30 cases of 2-arc VMAT plans including first optimization and final optimization with/without vector-model-supported optimization, were compared using the 2-sided t-test and paired Wilcoxon signed rank test, with a significance level of 0.05 and a false discovery rate of less than 0.05. For S&S IMRT, 1-arc VMAT, and 2-arc VMAT prostate plans, there was a significant reduction in the planning time and iteration with vector-model-supported optimization by almost 50%. When the first optimization plans were compared, 2-arc VMAT prostate plans had better plan quality than 1-arc VMAT plans. The volume receiving 35 Gy in the femoral head for 2-arc VMAT plans was reduced with the vector-model-supported optimization compared with the traditional manual optimization approach. Otherwise, the quality of plans from both approaches was comparable. Vector-model-supported optimization was shown to offer much shortened planning time and iteration number without compromising the plan quality.
传统手动计划优化耗时较长,可能会限制步进式调强放疗/容积调强放疗(S&S IMRT/VMAT)的应用。基于从医学数字成像和通信(DICOM)文件中提取的结构和生理特征,开发了一个检索相似放疗病例的向量模型库。从选定的相似参考病例中检索计划参数,并将其应用于测试病例,以绕过计划参数的逐步调整。因此,可以减少在优化开始时传统试错手动优化方法所花费的计划时间。每个S&S IMRT/VMAT前列腺参考数据库包含100个先前治疗的病例。根据肿瘤学家的临床剂量处方,对前列腺病例进行传统优化和向量模型支持的优化重新计划。使用双侧t检验和配对Wilcoxon符号秩检验比较了总共360个计划,这些计划包括30例S&S IMRT、30例单弧VMAT和30例双弧VMAT计划,包括有无向量模型支持优化的首次优化和最终优化,显著性水平为0.05,错误发现率小于0.05。对于S&S IMRT、单弧VMAT和双弧VMAT前列腺计划,向量模型支持的优化使计划时间和迭代次数显著减少了近50%。当比较首次优化计划时,双弧VMAT前列腺计划的计划质量优于单弧VMAT计划。与传统手动优化方法相比,向量模型支持的优化减少了双弧VMAT计划中股骨头接受35 Gy剂量的体积。否则,两种方法的计划质量相当。结果表明,向量模型支持的优化在不影响计划质量的情况下,显著缩短了计划时间和迭代次数。