Sanchez-Parcerisa D, Cortés-Giraldo M A, Dolney D, Kondrla M, Fager M, Carabe A
Department of Radiation Oncology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA.
Phys Med Biol. 2016 Feb 21;61(4):1705-21. doi: 10.1088/0031-9155/61/4/1705. Epub 2016 Feb 3.
In order to integrate radiobiological modelling with clinical treatment planning for proton radiotherapy, we extended our in-house treatment planning system FoCa with a 3D analytical algorithm to calculate linear energy transfer (LET) in voxelized patient geometries. Both active scanning and passive scattering delivery modalities are supported. The analytical calculation is much faster than the Monte-Carlo (MC) method and it can be implemented in the inverse treatment planning optimization suite, allowing us to create LET-based objectives in inverse planning. The LET was calculated by combining a 1D analytical approach including a novel correction for secondary protons with pencil-beam type LET-kernels. Then, these LET kernels were inserted into the proton-convolution-superposition algorithm in FoCa. The analytical LET distributions were benchmarked against MC simulations carried out in Geant4. A cohort of simple phantom and patient plans representing a wide variety of sites (prostate, lung, brain, head and neck) was selected. The calculation algorithm was able to reproduce the MC LET to within 6% (1 standard deviation) for low-LET areas (under 1.7 keV μm(-1)) and within 22% for the high-LET areas above that threshold. The dose and LET distributions can be further extended, using radiobiological models, to include radiobiological effectiveness (RBE) calculations in the treatment planning system. This implementation also allows for radiobiological optimization of treatments by including RBE-weighted dose constraints in the inverse treatment planning process.
为了将放射生物学建模与质子放疗的临床治疗计划相结合,我们用一种三维分析算法扩展了我们的内部治疗计划系统FoCa,以计算体素化患者几何结构中的线能量转移(LET)。支持主动扫描和被动散射两种输送方式。该分析计算比蒙特卡罗(MC)方法快得多,并且可以在逆向治疗计划优化套件中实现,使我们能够在逆向计划中创建基于LET的目标。LET是通过将一维分析方法(包括对二次质子的一种新校正)与笔形束类型的LET核相结合来计算的。然后,将这些LET核插入FoCa中的质子卷积叠加算法中。分析得到的LET分布以在Geant4中进行的MC模拟为基准。选择了一组代表各种部位(前列腺、肺、脑、头颈部)的简单体模和患者计划。对于低LET区域(低于1.7 keV·μm(-1)),该计算算法能够将MC LET再现到6%(1个标准差)以内,对于高于该阈值的高LET区域,再现误差在22%以内。利用放射生物学模型,剂量和LET分布可以进一步扩展,以在治疗计划系统中纳入放射生物学有效性(RBE)计算。这种实现方式还允许通过在逆向治疗计划过程中纳入RBE加权剂量约束来对治疗进行放射生物学优化。