Liu Jianfei, Wu Q Jackie, Kirkpatrick John P, Yin Fang-Fang, Yuan Lulin, Ge Yaorong
Department of Radiation Oncology, Duke University Medical Centre, Durham, NC, USA
Phys Med Biol. 2015 Mar 7;60(5):N83-92. doi: 10.1088/0031-9155/60/5/N83.
Prediction of achievable dose distribution in spine stereotactic body radiation therapy (SBRT) can help in designing high-quality treatment plans to maximally protect spinal cords and to effectively control tumours. Dose distributions at spinal cords are primarily affected by the shapes of adjacent planning target volume (PTV) contours. In this work, we estimate such contour effects and predict dose distributions by exploring active optical flow model (AOFM) and active shape model (ASM). We first collect a sequence of dose sub-images and PTV contours near spinal cords from fifteen SBRT plans in the training dataset. The data collection is then classified into five groups according to the PTV locations in relation to spinal cords. In each group, we randomly choose a dose sub-image as the reference and register all other sub-images to the reference using an optical flow method. AOFM is then constructed by importing optical flow vectors and dose values into the principal component analysis (PCA). Similarly, we build ASM by using PCA on PTV contour points. The correlation between ASM and AOFM is estimated via a stepwise multiple regression model. When predicting dose distribution of a new case, the group is first determined based on the PTV contour. The prediction model of the selected group is used to estimate dose distributions by mapping the PTV contours from the ASM space to the AOFM space. This method was validated on fifteen SBRT plans in the testing dataset. Analysis of dose-volume histograms revealed that the important D2%, D5%, D10% and D0.1cc dosimetric parameters of spinal cords between the prediction and the clinical plans were 11.7 ± 1.7 Gy versus 11.8 ± 1.7 Gy (p = 0.95), 10.9 ± 1.7 Gy versus 11.1 ± 1.9 Gy (p = 0.8295), 10.2 ± 1.6 Gy versus 10.1 ± 1.7 (p = 0.9036) and 11.2 ± 2.0 Gy versus 11.1 ± 2.2 Gy (p = 0.5208), respectively. Here, the ‘cord’ is the spinal cord proper (not the thecal sac) extended 5 mm inferior and superior to the involved vertebral bodies, and the ‘PTV’ is the involved segment of the vertebral body expanded uniformly by 2 mm but excluding the spinal cord volume expanded by 2 mm (Ref. RTOG 0631). These results suggested that the AOFM-based approach is a promising tool for predicting accurate spinal cord dose in clinical practice. In this work, we demonstrated the feasibility of using AOFM and ASM models derived from previously treated patients to estimate the achievable dose distributions for new patients.
脊柱立体定向体部放射治疗（SBRT）中可实现剂量分布的预测有助于设计高质量的治疗计划，以最大程度地保护脊髓并有效控制肿瘤。脊髓处的剂量分布主要受相邻计划靶区（PTV）轮廓形状的影响。在这项研究中，我们通过探索有源光流模型（AOFM）和主动形状模型（ASM）来估计此类轮廓效应并预测剂量分布。我们首先从训练数据集中的15个SBRT计划中收集一系列靠近脊髓的剂量子图像和PTV轮廓。然后根据PTV相对于脊髓的位置将数据收集分为五组。在每组中，我们随机选择一个剂量子图像作为参考，并使用光流方法将所有其他子图像配准到该参考图像。然后通过将光流矢量和剂量值导入主成分分析（PCA）来构建AOFM。同样，我们通过对PTV轮廓点使用PCA来构建ASM。通过逐步多元回归模型估计ASM和AOFM之间的相关性。在预测新病例的剂量分布时，首先根据PTV轮廓确定组。所选组的预测模型用于通过将PTV轮廓从ASM空间映射到AOFM空间来估计剂量分布。该方法在测试数据集中的15个SBRT计划上得到了验证。剂量体积直方图分析表明，预测计划和临床计划之间脊髓的重要剂量学参数D2％、D5％、D10％和D0.1cc分别为11.7±1.7 Gy与11.8±1.7 Gy（p = 0.95）、10.9±1.7 Gy与11.1±1.9 Gy（p = 0.8295）、10.2±1.6 Gy与10.1±1.7（p = 0.9036）以及11.2±2.0 Gy与11.1±2.2 Gy（p = 0.5208）。此处，“脊髓”是指脊髓本身（而非硬膜囊），在受累椎体上下各延伸5 mm，“PTV”是指椎体的受累节段均匀扩展2 mm，但不包括扩展2 mm的脊髓体积（参考文献RTOG 0631）。这些结果表明，基于AOFM的方法是在临床实践中预测准确脊髓剂量的有前途的工具。在这项研究中，我们证明了使用从先前治疗的患者中得出的AOFM和ASM模型来估计新患者可实现的剂量分布的可行性。
