Department of Radiation Oncology, Virginia Commonwealth University, P.O. Box 980058, Richmond, Virginia 23298, USA.
Med Phys. 2010 Feb;37(2):550-63. doi: 10.1118/1.3273063.
This work (i) proposes a probabilistic treatment planning framework, termed coverage optimized planning (COP), based on dose coverage histogram (DCH) criteria; (ii) describes a concrete proof-of-concept implementation of COP within the PINNACLE treatment planning system; and (iii) for a set of 28 prostate anatomies, compares COP plans generated with this implementation to traditional PTV-based plans generated with planning criteria approximating those in the high dose arm of the Radiation Therapy Oncology Group 0126 protocol. Let Dv denote the dose delivered to fractional volume v of a structure. In conventional intensity modulated radiation therapy planning, Dv has a unique value derived from the static (planned) dose distribution. In the presence of geometric uncertainties (e.g., setup errors) Dv assumes a range of values. The DCH is the complementary cumulative distribution function of D(v+). DCHs are similar to dose volume histograms (DVHs). Whereas a DVH plots volume v versus dose D, a DCH plots coverage probability Q versus D. For a given patient, Q is the probability (i.e., percentage of geometric uncertainties) for which the realized value of Dv exceeds D. PTV-based treatment plans can be converted to COP plans by replacing DVH optimization criteria with corresponding DCH criteria. In this approach, PTVs and planning organ at risk volumes are discarded, and DCH criteria are instead applied directly to clinical target volumes (CTVs) or organs at risk (OARs). Plans are optimized using a similar strategy as for DVH criteria. The specific implementation is described. COP was found to produce better plans than standard PTV-based plans, in the following sense. While target OAR dose tradeoff curves were equivalent to those for PTV-based plans, COP plans were able to exploit slack in OAR doses, i.e., cases where OAR doses were below their optimization limits, to increase target coverage. Specifically, because COP plans were not constrained by a predefined PTV, they were able to provide wider dosimetric margins around the CTV, by pushing OAR doses up to, but not beyond, their optimization limits. COP plans demonstrated improved target coverage when averaged over all 28 prostate anatomies, indicating that the COP approach can provide benefits for many patients. However, the degree to which slack OAR doses can be exploited to increase target coverage will vary according to the individual patient anatomy. The proof-of-concept COP implementation investigated here utilized a probabilistic DCH criteria only for the CTV minimum dose criterion. All other optimization criteria were conventional DVH criteria. In a mature COP implementation, all optimization criteria will be DCH criteria, enabling direct planning control over probabilistic dose distributions. Further research is necessary to determine the benefits of COP planning, in terms of tumor control probability and/or normal tissue complication probabilities.
（i）提出了一种基于剂量覆盖直方图（DCH）准则的概率治疗计划框架，称为覆盖优化计划（COP）；（ii）在 PINNACLE 治疗计划系统中描述了 COP 的具体概念验证实现；（iii）针对 28 个前列腺解剖结构，将使用此实现生成的 COP 计划与使用近似于放射治疗肿瘤学组 0126 协议高剂量臂中的计划标准生成的传统基于 PTV 的计划进行比较。让 Dv 表示结构的分数体积 v 所接受的剂量。在传统的强度调制放射治疗计划中，Dv 具有从静态（计划）剂量分布中得出的唯一值。在存在几何不确定性（例如，设置误差）的情况下，Dv 会假定一系列值。DCH 是 D(v+）的互补累积分布函数。DCH 类似于剂量体积直方图（DVH）。虽然 DVH 绘制体积 v 与剂量 D 的关系，但 DCH 则绘制覆盖概率 Q 与 D 的关系。对于给定的患者，Q 是 Dv 的实际值超过 D 的几何不确定性的概率（即百分比）。基于 PTV 的治疗计划可以通过将 DVH 优化标准替换为相应的 DCH 标准来转换为 COP 计划。在这种方法中，PTV 和计划器官危及体积被丢弃，而是直接将 DCH 标准应用于临床靶体积（CTV）或器官危及（OAR）。使用与 DVH 标准类似的策略对计划进行优化。描述了具体的实现。COP 被发现可以产生比标准基于 PTV 的计划更好的计划，其意义在于。虽然目标 OAR 剂量权衡曲线与基于 PTV 的计划等效，但 COP 计划能够利用 OAR 剂量的松弛，即 OAR 剂量低于其优化限制的情况，以增加目标覆盖。具体来说，由于 COP 计划不受预定义 PTV 的限制，因此它们能够通过将 OAR 剂量推至但不超过其优化限制来在 CTV 周围提供更宽的剂量学边缘。COP 计划在所有 28 个前列腺解剖结构上的平均目标覆盖情况得到了改善，这表明 COP 方法可以为许多患者带来益处。然而，利用松弛的 OAR 剂量来增加目标覆盖的程度将根据患者的个体解剖结构而有所不同。这里研究的概念验证 COP 实现仅对 CTV 最小剂量标准使用了概率 DCH 标准。所有其他优化标准都是传统的 DVH 标准。在成熟的 COP 实现中，所有优化标准都将是 DCH 标准，从而能够直接控制概率剂量分布。需要进一步研究以确定 COP 计划在肿瘤控制概率和/或正常组织并发症概率方面的优势。
