Rickhey Mark, Koelbl Oliver, Eilles Christoph, Bogner Ludwig
Department of Radiation Oncology, University Hospital Regensburg, Regensburg, Germany.
Strahlenther Onkol. 2008 Oct;184(10):536-42. doi: 10.1007/s00066-008-1883-6. Epub 2008 Oct 1.
To demonstrate the feasibility of a biologically adapted dose-escalation approach to brain tumors.
Due to the specific accumulation of fluoroethyltyrosine (FET) in brain tumors, (18)F-FET-PET imaging is used to derive a voxel-by-voxel dose distribution. Although the kinetics of (18)F-FET are not completely understood, the authors regard regions with high tracer uptake as vital and aggressive tumor and use a linear dose-escalation function between SUV (standard uptake value) 3 and SUV 5. The resulting dose distribution is then planned using the inverse Monte Carlo treatment- planning system IKO. In a theoretical study, the dose range is clinically adapted from 1.8 Gy to 2.68 Gy per fraction (with a total of 30 fractions). In a second study, the maximum dose of the model is increased step by step from 2.5 Gy to 3.4 Gy to investigate whether a significant dose escalation to tracer-accumulating subvolumes is possible without affecting the shell-shaped organ at risk (OAR). For all dose-escalation levels the dose difference Delta D of each voxel inside the target volume is calculated and the mean dose difference Delta D and their standard deviation sigma Delta D are determined. The dose to the OAR is evaluated by the dose values D OAR 50% and D OAR 5%, which are the dose values not exceeded by 50% and 5% of the volume, respectively.
The inhomogeneous dose prescription is achieved with high accuracy (Delta D < 0.03 +/- 0.3 Gy/fraction). The maximum dose can be increased remarkably, without increasing the dose to the OAR (standard deviation of D OAR 50% < 0.02 Gy/fraction and of D OAR 5% < 0.05 Gy/fraction).
Assuming that regions with high tracer uptake can be interpreted as target for radiotherapy, (18)F-FET-PET-based "dose painting by numbers" applied to brain tumors is a feasible approach. The dose, and therefore potentially the chance of tumor control, can be enhanced. The proposed model can easily be transferred to other tracers and tumor entities.
证明生物适应性剂量递增方法用于脑肿瘤治疗的可行性。
由于氟乙基酪氨酸（FET）在脑肿瘤中的特异性积聚，利用（18）F-FET-PET成像得出逐体素剂量分布。尽管（18）F-FET的动力学尚未完全明确，但作者将示踪剂摄取高的区域视为重要且侵袭性强的肿瘤，并在SUV（标准摄取值）3至SUV 5之间使用线性剂量递增函数。然后使用逆蒙特卡罗治疗计划系统IKO对所得剂量分布进行规划。在一项理论研究中，临床适用的剂量范围为每分次1.8 Gy至2.68 Gy（共30分次）。在第二项研究中，将模型的最大剂量从2.5 Gy逐步增加至3.4 Gy，以研究在不影响壳状危及器官（OAR）的情况下，是否有可能对示踪剂积聚的子体积进行显著的剂量递增。对于所有剂量递增水平，计算靶区内每个体素的剂量差异ΔD，并确定平均剂量差异ΔD及其标准差σΔD。通过剂量值D OAR 50%和D OAR 5%评估OAR的剂量，这两个剂量值分别是不超过50%和5%体积的剂量值。
以高精度实现了非均匀剂量处方（ΔD < 0.03 ± 0.3 Gy/分次）。最大剂量可显著增加，而不增加OAR的剂量（D OAR 50%的标准差 < 0.02 Gy/分次，D OAR 5%的标准差 < 0.05 Gy/分次）。
假设示踪剂摄取高的区域可被解释为放射治疗的靶区，应用于脑肿瘤的基于（18）F-FET-PET的“数字式剂量描绘”是一种可行的方法。可以提高剂量，从而可能增加肿瘤控制的机会。所提出的模型可轻松应用于其他示踪剂和肿瘤类型。
