Reduced-Order Constrained Optimization for Rapid IMRT and VMAT Treatment Planning

描述（由申请人提供）：在过去十年中，调强放射疗法（IMRT）已经彻底改变了癌症的治疗，因为它可以紧密地符合并逐步增加对肿瘤的辐射剂量，同时保护附近的辐射敏感的正常组织，导致比以前的技术更好的局部控制和更少的治疗后并发症。然而，获得用于困难部位的临床上可接受的IMRT计划的过程仍然极其缓慢，需要忙碌的专家在参数调整的手动试错循环中花费许多小时的时间。该项目的目标是使用一种新的自动化方法，以计算上易于处理且具有临床意义的方式直接应用约束优化，从而大幅减少获得临床可接受的调强放射治疗计划的时间。假设使用这种技术的临床治疗计划时间将从几个小时减少到几分钟。 这种新方法被称为ROCO（降阶约束优化），它将优化和机器学习理论中的成熟概念转化为IMRT规划的新应用，利用无约束优化的速度和易用性，并引入降维步骤，使真正的约束优化变得易于处理。该提案的具体目标是：（1）将降阶约束优化应用于计划过程非常耗时的非小细胞肺癌和鼻咽癌的IMRT计划;（2）开发并扩展降阶约束优化范例，以用于前列腺部位的有前途的IMRT变体，称为体积调制弧治疗（VMAT），这是目前临床上几乎难以计划;和（3）集成新的工具到临床IMRT计划过程中，在纪念斯隆-凯特琳癌症中心，使用动力研究，以验证假设，提出的方法显着提高规划速度。将与专家临床治疗规划师和生物统计学家协商设计实验，并使用每个研究中心约50名患者的匿名数据进行仔细验证。 所提出的方法的主要好处是大大减少规划时间，这是至关重要的，如果调强放射治疗和VMAT是在临床应用中发挥其全部潜力。在忙碌的诊所中，长时间的计划时间对可用资源造成严重压力，并且可能导致治疗延迟、接受次优计划，或者在最坏的情况下，由于时间压力而导致错误。从长远来看，所提出的方法将提供更深入的了解剂量优化问题的关键要素，显着减少目前的调强放射治疗计划的试错努力的特点，并减少治疗计划选择的主观性。 公共卫生相关性：调强放射治疗（IMRT）是一种非常有前途的癌症治疗方法，但目前需要专家治疗计划者花费数小时的时间来获得满足医生指定的所有临床约束的计划。该提议描述了一种用于治疗计划优化的新的自动方法，该方法非常快速，仅需要几分钟来产生直接满足所有医师约束的计划，从而可能为忙碌的诊所节省大量时间。

DESCRIPTION (provided by applicant): Intensity-modulated radiotherapy (IMRT) has revolutionized the treatment of cancers in the last decade, since it can tightly conform and escalate radiation dose to a tumor while simultaneously protecting nearby radiation- sensitive normal tissues, resulting in better local control and fewer post-treatment complications than previous techniques. However, the process of obtaining a clinically acceptable IMRT plan for a difficult site is still extremely slow, requiring many hours of a busy expert's time in a manual trial-and-error loop of parameter adjustment. The goal of this project is to drastically reduce the amount of time to obtain a clinically acceptable IMRT plan using a new automated method that directly applies constrained optimization in a computationally tractable and clinically meaningful way. The hypothesis is that clinical treatment planning times using this technique will be reduced from several hours to a matter of minutes. The new approach, called ROCO (Reduced-Order Constrained Optimization) translates well-established concepts from optimization and machine learning theory to the novel application of IMRT planning, exploiting the speed and ease of unconstrained optimizations and introducing a dimensionality reduction step that makes true constrained optimization tractable. The Specific Aims of the proposal are to (1) apply Reduced-Order Con- strained Optimization to IMRT planning for non-small cell lung cancers and nasopharynx cancers, where the planning process is highly time-consuming; (2) develop and extend the Reduced-Order Constrained Optimization paradigm to a promising IMRT variant called Volumetric Modulated Arc Therapy (VMAT) for the prostate site, which is currently nearly clinically intractable to plan; and (3) integrate the new tools into the clinical IMRT planning process at Memorial Sloan-Kettering Cancer Center, using a powered study to verify the hypothesis that the proposed method significantly improves planning speed. The experiments will be designed in consultation with an expert clinical treatment planner and biostatistician, and carefully validated using anonymized data from approximately 50 patients for each site. The main benefit of the proposed approach is to drastically reduce planning times, which is critical if IMRT and VMAT are to reach their full potential in clinical application. In a busy clinic, long planning times place a severe stress on available resources, and can result in treatment delays, acceptance of sub-optimal plans or - in the worst case - errors due to time pressure. In the longer term, the proposed approach will provide deeper insight into the critical elements of the dose optimization problem, significantly reduce the trial-and-error effort characteristic of current IMRT planning, and reduce subjectivity in treatment plan selection. PUBLIC HEALTH RELEVANCE: Intensity-modulated radiation therapy (IMRT) is an extremely promising cancer treatment, but currently requires many hours of an expert treatment planner's time to obtain a plan that meets all the clinical constraints specified by the physician. This proposal describes a new, automatic method for treatment plan optimization that is very fast, requiring only minutes to produce a plan that directly meets all the physician's constraints, potentially saving much time for a busy clinic.