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Reduced-order constrained optimization (ROCO): Clinical application to head-and-neck IMRT

机译:降阶约束优化(ROCO):头颈IMRT的临床应用

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>Purpose: The authors present the application of the reduced order constrained optimization (ROCO) method, previously successfully applied to the prostate and lung sites, to the head-and-neck (H&N) site, demonstrating that it can quickly and automatically generate clinically competitive IMRT plans. We provide guidelines for applying ROCO to larynx, oropharynx, and nasopharynx cases, and report the results of a live experiment that demonstrates how an expert planner can save several hours of trial-and-error interaction using the proposed approach.>Methods: The ROCO method used for H&N IMRT planning consists of three major steps. First, the intensity space of treatment plans is sampled by solving a series of unconstrained optimization problems with a parameter range based on previously treated patient data. Second, the dominant modes in the intensity space are estimated by dimensionality reduction using principal component analysis (PCA). Third, a constrained optimization problem over this basis is quickly solved to find an IMRT plan that meets organ-at-risk (OAR) and target coverage constraints. The quality of the plan is assessed using evaluation tools within Memorial Sloan-Kettering Cancer Center (MSKCC)’s treatment planning system (TPS).>Results: The authors generated ten H&N IMRT plans for previously treated patients using the ROCO method and processed them for deliverability by a dynamic multileaf collimator (DMLC). The authors quantitatively compared the ROCO plans to the previously achieved clinical plans using the TPS tools used at MSKCC, including DVH and isodose contour analysis, and concluded that the ROCO plans would be clinically acceptable. In our current implementation, ROCO H&N plans can be generated using about 1.6 h of offline computation followed by 5–15 min of semiautomatic planning time. Additionally, the authors conducted a live session for a plan designated by MSKCC performed together with an expert H&N planner. A technical assistant set up the first two steps, which were performed without further human interaction, and then collaborated in a virtual meeting with the expert planner to perform the third (constrained optimization) step. The expert planner performed in-depth analysis of the resulting ROCO plan and deemed it to be clinically acceptable and in some aspects superior to the clinical plan. This entire process took 135 min including two constrained optimization runs, in comparison to the estimated 4 h that would have been required using traditional clinical planning tools.>Conclusions: The H&N site is very challenging for IMRT planning, due to several levels of prescription and a large, variable number (6–20) of OARs that depend on the location of the tumor. ROCO for H&N shows promise in generating clinically acceptable plans both more quickly and with substantially less human interaction.
机译:>目的:作者介绍了先前成功应用于前列腺和肺部,头颈部(H&N)的降阶约束优化(ROCO)方法的应用,证明了这种方法可以快速自动生成具有临床竞争力的IMRT计划。我们提供了将ROCO应用于喉,口咽和鼻咽病例的指南,并报告了一项现场实验的结果,该结果证明了专家计划人员可以使用建议的方法节省数小时的反复试验。>方法: 用于H&N IMRT规划的ROCO方法包括三个主要步骤。首先,通过基于先前治疗的患者数据,通过参数范围解决一系列无约束的优化问题,对治疗计划的强度空间进行采样。其次,使用主成分分析(PCA)通过降维来估计强度空间中的主导模式。第三,在此基础上的约束优化问题可以快速解决,以找到满足器官风险(OAR)和目标覆盖率约束的IMRT计划。该计划的质量是使用纪念斯隆-凯特琳癌症中心(MSKCC)的治疗计划系统(TPS)中的评估工具进行评估的。>结果:作者针对先前接受过治疗的患者使用以下方法生成了十份H&N IMRT计划: ROCO方法,并通过动态多叶准直仪(DMLC)对它们进行处理以实现可交付性。作者使用MSKCC所使用的TPS工具(包括DVH和等剂量轮廓分析)将ROCO计划与先前实现的临床计划进行了定量比较,并得出结论ROCO计划在临床上是可以接受的。在我们当前的实施中,可以使用大约1.6小时的离线计算以及5–15分钟的半自动计划时间来生成ROCO H&N计划。此外,作者还为MSKCC指定的计划与专家H&N计划者一起进行了现场会议。技术助理设置了前两个步骤,无需进一步的人工干预即可执行,然后与专家计划人员在虚拟会议中进行协作以执行第三步(约束优化)。专家计划人员对生成的ROCO计划进行了深入分析,并认为该计划在临床上是可以接受的,并且在某些方面优于临床计划。整个过程耗时135分钟,包括两次受限的优化运行,而使用传统的临床计划工具则需要大约4小时。>结论:H&N网站对于IMRT计划非常具有挑战性到不同级别的处方,以及取决于肿瘤位置的大量可变(6–20)OAR。 ROCO for H&N展示了更快,更少人与人之间产生临床可接受计划的前景。

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