Improving Cancer Treatment Planning by DMH-Based Inverse Optimization

描述(申请人提供)：癌症患者仍然是一个具有挑战性的疾病群体，根据目前的治疗计划和交付实践，他们面临着相当糟糕的预后。显然需要通过为这些患者提供创新的治疗方法，为潜在的剂量增加和/或增加健康组织保留提供场所。目前最先进的放射治疗计划依赖于剂量-体积-直方图(DVH)范例，其中剂量到解剖结构的分数(最常见)或绝对体积在优化和计划评估过程中都被使用。然而，有人认为，当剂量-质量-直方图(DMH)被考虑在治疗计划评估中时，递送剂量的影响似乎与健康组织毒性(从而与临床结果)更密切相关。我们建议将质量和密度信息显式地合并到逆优化过程的成本函数中，从而从DVH到DMH治疗计划范式转变。这种新的基于DMH的调强放射治疗(IMRT)优化旨在将特定质量的健康组织而不是体积的辐射剂量降至最低。我们的工作假设是DMH优化将大幅减少对健康组织的剂量。在某些情况下，由于疾病广泛，难以治疗，健康组织的较低剂量可用于等毒剂量的升级，这可能导致估计的局部区域肿瘤控制概率增加约两倍。为了验证这一假设，我们将追求以下具体目标：(1)建立基于DMH的调强放疗优化的理论和计算框架。该框架将包括3D和4D调强放射治疗以及针对不同解剖位置的3D体积调制弧线(VMAT)规划。(2)研究了DMH优化函数的不同参数形式。最终目标将是同时最小化健康组织剂量和/或增加治疗剂量，而不违反健康解剖结构的既定剂量容限。以及(3)这一新的优化范例的实际实施和应用，其中将对肺癌、头颈部和前列腺癌病例进行虚拟临床试验。DMH优化剂量学改进比护理标准DVH优化的统计意义将被量化。前瞻性的3D和4D CT数据收集将用于研究肿瘤时间趋势变化和基于DMH的优化结果之间的交互作用。4D CT数据也将被用来调查和量化基于DMH的终点与放射治疗期间和之后肺功能丧失之间的相关性。我们的3D VMAT和3D/4D调强放射治疗计划的交付能力(使用现有的放射治疗设备)将得到实验验证，从而为启动临床试验铺平道路。

DESCRIPTION (provided by applicant): Cancer patients continue to represent a challenging disease population, which faces rather poor prognosis with current treatment planning and delivery practices. Venues for a potential dose escalation and/or increased healthy tissue sparing, through innovative therapeutic approaches for those patients, are clearly needed. Current state of the art radiotherapy treatment planning relies on the dose-volume-histogram (DVH) paradigm, where doses to fractional (most often) or absolute volumes of anatomical structures are employed in both optimization and plan evaluation process. It has been argued however, that the effects of delivered dose seem to be more closely related to healthy tissue toxicity (and thereby to clinical outcomes) when dose-mass- histograms (DMHs) are considered in treatment plan evaluation. We propose the incorporation of mass and density information explicitly into the cost functions of the inverse optimization process, thereby shifting from DVH t DMH treatment planning paradigm. This novel DMH-based intensity modulated radiotherapy (IMRT) optimization aims in minimization of radiation doses to a certain mass, rather than a volume, of healthy tissue. Our working hypothesis is that DMH- optimization will reduce doses to healthy tissue substantially. In certain cases, with extensive, difficult to treat disease, lower doses to healthy tissue can be used for isotoxic dose escalation, which may result in an approximately two-fold increase in estimated loco-regional tumor control probability. To test this hypothesis we will pursue the following specific aims: (1) Develop the theoretical and computational framework of the DMH-based IMRT optimization. This framework will incorporate 3D and 4D IMRT as well as 3D volumetric modulated arc (VMAT) planning for different anatomical sites. (2) Investigate different parametric forms for DMH-optimization functions. The ultimate goal would be the simultaneous minimization of healthy tissue doses and/or escalation of therapeutic doses, without violating the established dosimetric tolerances for healthy anatomical structures. And (3) Practical implementation and application of this novel optimization paradigm, where virtual clinical trials for cohorts of lung, head-and-neck, and prostate cancer cases will be performed. Statistical significance of the DMH-optimization dosimetric improvements over standard of care DVH-optimization will be quantified. Prospective 3D and 4D CT data collection will be used to study the interactions between tumor time-trending changes and DMH-based optimization results. 4D CT data will also be used to investigate and quantify the correlation between DMH-based end points and the loss of pulmonary function during and after radiotherapy treatment. The deliverability (with the existing radiotherapy treatment equipment) of our 3D VMAT and 3D/4D IMRT plans will be experimentally verified, thereby paving the road for initiation of clinical trials.