School of Medicine, University of Maryland, Baltimore, MD, USA.
Rigshospitalet, University of Copenhagen, Copenhagen, Denmark.
Med Phys. 2018 Nov;45(11):5332-5342. doi: 10.1002/mp.13211. Epub 2018 Oct 17.
Current radiation therapy planning uses a set of defined dose-volume constraints to ensure a specified level of tumor coverage while constraining the dose distribution in the organs at risk. Such constraints are aggregated, population-based quantities that do not adequately consider patient-specific risk factors. Furthermore, these constraints are calculated for each organ independently and it is therefore not guaranteed that the optimal trade-off between organs is achieved. We introduce a novel radiotherapy planning approach where a patient-specific all-cause mortality risk is minimized using inverse plan optimization. As illustration of concept, our outcome risk model incorporates patient age, sex, cardiac risk factor (CRF), and smoking.
We retrospectively analyzed a left-sided breast cancer case and a Hodgkin's lymphoma case, both clinically treated with three-dimensional conformal radiotherapy (3D-CRT). Our objective function for inverse plan optimization was an equally weighted summation of risk models for cancer recurrence and mortality from radiation-induced coronary heart disease and secondary lung and breast cancers incorporating patient age, sex, CRF, and smoking. We allowed the optimization algorithm to choose from a large set of gantry angles. The optimization task was to choose beams and optimize monitor units (MUs) so that overall survival was maximized (and the total risk of cancer recurrence and mortality from radiation-induced causes were minimized). The sensitivity analysis was performed in the lymphoma case by changing the tumor control probability model from using mean dose (Model 1) to using generalized equivalent uniform dose (Model 2).
For the breast case in this study, the 3D-CRT clinical plan used eight beams while the proposed 3D-CRT outcome-optimized plan used five beams, reducing the total risk - summation of the risks of recurrence and secondary disease mortality - from 3% to 2%. The mean doses to clinical target volume (CTV) and internal mammary nodes (IMN) were increased in the outcome-optimized plan by 1.9 and 1.8 Gy, respectively. For the Hodgkin's lymphoma case, the clinical 3D-CRT plan used two beams, while the proposed 3D-CRT outcome-optimized plan used three beams, reducing the total risk by 6% (from 16% to 10%). Using either of the two tumor control models for the lymphoma case resulted in outcome-optimized plans where tumor control was compensated at the cost of saving organs at risk. However, the impact of sensitivity to models was comparatively large. Using Model 1 resulted in a reduction in mean target dose by 15.2 vs 7.1 Gy for Model 2. In all cases, the chosen beams in outcome-optimized plans were different from clinically used beams.
The proposed optimization strategy, supplanting dosimetric objectives with comprehensive individual risk estimates, has the potential to yield improved outcomes in terms of reduced mortality risk in cancer patients treated with radiotherapy. The approach is, however, currently limited by gaps in knowledge about the effect of compromising dose to part of the target, for example, in order to spare cardiac structures.
目前的放射治疗计划使用一组定义的剂量-体积限制来确保指定的肿瘤覆盖水平，同时限制危险器官中的剂量分布。这些限制是聚合的、基于人群的数量，不能充分考虑患者的特定风险因素。此外，这些限制是为每个器官独立计算的，因此不能保证在器官之间达到最佳的权衡。我们引入了一种新的放射治疗计划方法，通过逆计划优化使患者的全因死亡率最小化。作为概念说明，我们的结果风险模型包括患者年龄、性别、心脏风险因素（CRF）和吸烟。
我们回顾性分析了一个左侧乳腺癌病例和一个霍奇金淋巴瘤病例，两者均采用三维适形放射治疗（3D-CRT）进行临床治疗。我们的逆计划优化目标函数是对癌症复发和由放射引起的冠心病以及继发性肺癌和乳腺癌引起的死亡率的风险模型进行加权求和，同时考虑患者的年龄、性别、CRF 和吸烟。我们允许优化算法从一组大角度集选择。优化任务是选择光束并优化监测单位（MU），以使总生存率最大化（并且由辐射引起的癌症复发和死亡率的总风险最小化）。通过将肿瘤控制概率模型从使用平均剂量（模型 1）更改为使用广义等效均匀剂量（模型 2），在淋巴瘤病例中进行了敏感性分析。
对于本研究中的乳房病例，3D-CRT 临床计划使用了 8 束光束，而提出的 3D-CRT 结果优化计划仅使用了 5 束光束，从而将总风险（复发和继发疾病死亡率的风险总和）从 3%降低到 2%。结果优化计划中临床靶区（CTV）和内乳淋巴结（IMN）的平均剂量分别增加了 1.9 和 1.8 Gy。对于霍奇金淋巴瘤病例，临床 3D-CRT 计划使用了 2 束光束，而提出的 3D-CRT 结果优化计划使用了 3 束光束，总风险降低了 6%（从 16%降至 10%）。对于淋巴瘤病例，使用两种肿瘤控制模型中的任何一种，都会导致结果优化计划以牺牲危险器官为代价来补偿肿瘤控制。然而，模型的敏感性的影响相对较大。使用模型 1 导致目标平均剂量降低 15.2 Gy，而使用模型 2 则降低 7.1 Gy。在所有情况下，结果优化计划中选择的光束与临床使用的光束不同。
用综合个体风险估计替代剂量学目标的建议优化策略有可能降低接受放疗的癌症患者的死亡率风险，从而改善治疗结果。然而，该方法目前受到有关部分靶区剂量妥协影响的知识差距的限制，例如为了保护心脏结构。
