Department of Radiation Physics, University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA.
Int J Radiat Oncol Biol Phys. 2012 Mar 15;82(4):1549-55. doi: 10.1016/j.ijrobp.2011.05.007. Epub 2011 Jul 6.
Studies have proposed that patients who receive radiation therapy to the base of the lung are more susceptible to radiation pneumonitis than patients who receive therapy to the apex of the lung. The primary purpose of the present study was to develop a novel method to incorporate the lung dose spatial information into a predictive radiation pneumonitis model. A secondary goal was to apply the method to a 547 lung cancer patient database to determine whether including the spatial information could improve the fit of our model.
The three-dimensional dose distribution of each patient was mapped onto one common coordinate system. The boundaries of the coordinate system were defined by the extreme points of each individual patient lung. Once all dose distributions were mapped onto the common coordinate system, the spatial information was incorporated into a Lyman-Kutcher-Burman predictive radiation pneumonitis model. Specifically, the lung dose voxels were weighted using a user-defined spatial weighting matrix. We investigated spatial weighting matrices that linearly scaled each dose voxel according to the following orientations: superior-inferior, anterior-posterior, medial-lateral, left-right, and radial. The model parameters were fit to our patient cohort with the endpoint of severe radiation pneumonitis. The spatial dose model was compared against a conventional dose-volume model to determine whether adding a spatial component improved the fit of the model.
Of the 547 patients analyzed, 111 (20.3%) experienced severe radiation pneumonitis. Adding in a spatial parameter did not significantly increase the accuracy of the model for any of the weighting schemes.
A novel method was developed to investigate the relationship between the location of the deposited lung dose and pneumonitis rate. The method was applied to a patient database, and we found that for our patient cohort, the spatial location does not influence the risk of pneumonitis.
研究表明，接受肺部基底放射治疗的患者比接受肺部尖部放射治疗的患者更容易发生放射性肺炎。本研究的主要目的是开发一种新的方法，将肺部剂量空间信息纳入预测放射性肺炎模型中。次要目的是将该方法应用于 547 例肺癌患者数据库，以确定纳入空间信息是否可以改善我们模型的拟合度。
将每位患者的三维剂量分布映射到一个共同的坐标系上。坐标系的边界由每个患者肺部的极值定义。一旦所有剂量分布都被映射到共同的坐标系上，空间信息就被纳入到 Lyman-Kutcher-Burman 预测放射性肺炎模型中。具体来说，使用用户定义的空间加权矩阵对肺部剂量体素进行加权。我们研究了根据以下方向线性缩放每个剂量体素的空间加权矩阵：上下、前后、内外、左右和放射状。使用严重放射性肺炎作为终点，将模型参数拟合到我们的患者队列中。将空间剂量模型与传统的剂量-体积模型进行比较，以确定添加空间成分是否可以改善模型的拟合度。
在分析的 547 名患者中，有 111 名（20.3%）发生了严重放射性肺炎。对于任何加权方案，添加空间参数都没有显著提高模型的准确性。
开发了一种新的方法来研究肺部沉积剂量的位置与肺炎发生率之间的关系。该方法应用于患者数据库，我们发现对于我们的患者队列，空间位置不影响肺炎的风险。
