Improving Cancer Treatment Planning by DMH-Based Inverse Optimization

描述（由申请人提供）：癌症患者仍然是一个具有挑战性的疾病人群，在目前的治疗计划和交付实践中，他们面临着相当差的预后。显然需要通过创新的治疗方法对这些患者进行潜在的剂量递增和/或增加健康组织保留的场所。目前最先进的放射治疗计划依赖于剂量-体积-直方图（DVH）范式，其中在优化和计划评估过程中使用了解剖结构的分数（最常见）或绝对体积的剂量。然而，有人认为，当在评估治疗计划时考虑剂量-质量直方图（DMHs）时，给药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明确地纳入逆向优化过程的成本函数中，从而从DVH转向DMH处理规划范式。这种新型的基于dmh的调强放疗（IMRT）优化旨在将辐射剂量最小化到一定质量，而不是健康组织的体积。我们的工作假设是，DMH优化将大大减少对健康组织的剂量。在某些情况下，对于广泛的、难以治疗的疾病，可以使用较低剂量的健康组织来增加等毒性剂量，这可能导致局部区域肿瘤控制的估计概率增加约两倍。为了验证这一假设，我们将追求以下具体目标：(1)开发基于dmh的IMRT优化的理论和计算框架。该框架将结合3D和4D IMRT以及3D体积调制弧线（VMAT）规划不同的解剖部位。(2)研究dmh优化函数的不同参数形式。最终目标将是在不违反健康解剖结构的既定剂量耐受性的情况下，同时使健康组织剂量最小化和/或治疗剂量增加。(3)该新型优化范式的实际实施和应用，将对肺癌、头颈癌和前列腺癌病例进行虚拟临床试验。dmh优化的剂量学改进相对于dvh优化的护理标准的统计学意义将被量化。前瞻性3D和4D CT数据收集将用于研究肿瘤时间趋势变化与基于dmh的优化结果之间的相互作用。4D CT数据也将用于研究和量化放疗期间和放疗后dmh终点与肺功能丧失之间的相关性。我们的3D VMAT和3D/4D IMRT计划的可交付性（在现有放疗治疗设备下）将进行实验验证，从而为开展临床试验铺平道路。

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.