Normal Tissue Complication Modeling for Radiotherapy

描述（由申请人提供）：正常组织并发症概率（NTCP）模型可用于个体化放射治疗计划，可能通过指导剂量分布的设计。我们的目标是根据剂量-容量、患者和疾病特征，为头颈部和肺部治疗并发症建立改进的NTCP模型。在之前的资助下，我们开发了一个软件系统，可以构建和方便地分析三维治疗方案数据库，包括来自多个机构的数据集。利用由此获得的数据，我们将使用多度量逻辑回归方法构建预测模型，其中包括剂量-体积项以及其他患者和疾病相关因素。变量选择的稳健性将用bootstrap方法进行测试。对于肺部治疗方案，我们将使用一种新的基于蒙特卡罗的技术重新计算肺和食管剂量-体积直方图，以提高数据库剂量分布的一致性和准确性。在特定目标（SA） #1下，rt后晚期肺炎/纤维化NTCP模型的改进，我们将：(a)扩大目前可用的Wash。Univ.数据集（来自166个点）。(b)研究纳入新的因素，如空间变化的敏感性和预处理肺功能测试，(c)使用RTOG 93-11数据集（113分）测试和完善我们的模型，(d)根据杜克大学和荷兰癌症研究所提供的数据（估计550分）测试和完善我们的模型。根据SA #2，急性食管炎NTCP模型的改进，我们将：(a)积累更多的患者（4年内从166例增加到估计450例），(b)纳入新的因素，如部分圆周照射和基于高剂量区域形状的其他指标，(c)使用荷兰癌症研究所提供的新数据（估计300例）测试和完善我们的模型。根据SA #3，放疗后腮腺唾液功能/口干模型的改进，我们将：(a)测试高剂量区域空间放置的影响，(b)使用该模型分析氨fostine在正在进行的强度调节放射治疗试验中对唾液功能的放射保护作用，以及(c)针对密歇根大学口干病数据集测试/完善我们的模型。此外，我们将利用方便和免费的软件工具建立公开存档的数据库。我们假设，这项研究将显著提高在个体化基础上预测口干症、肺炎或食管炎风险的能力，从而可能导致改进放射治疗。

DESCRIPTION (provided by applicant): Normal tissue complication probability (NTCP) models can be used to individualize radiation therapy treatment planning, potentially by guiding the design of the dose distribution. Our goal is to produce improved NTCP models, based on dose-volume, patient, and disease characteristics, for head and neck and lung treatment complications. Under the previous grant, we developed a software system, which enables the construction and convenient analysis of databases of 3-D treatment plans, including datasets from multiple institutions. Using data thus obtained, we will construct predictive models using multi-metric logistic regression methods, which include dose-volume terms as well as other patient and disease-related factors. The robustness of variable selection will be tested with bootstrap methods. For lung treatment plans, we will recompute lung and esophagus dose-volume histograms using a novel Monte Carlo-based technique, to improve the consistency and accuracy of the database dose distributions. Under Specific Aim (SA) #1, Improvements in post-RT late pneumonitis/fibrosis NTCP models, we will: (a) expand the currently available Wash. Univ. dataset (from 166 pts. to an estimated 450 in 4 years), (b) study the inclusion of new factors such as spatially-varying sensitivity and pretreatment pulmonary function tests, (c) test and refine our model using the RTOG 93-11 dataset (113 pts.), and (d) test and refine our model against data contributed by Duke University and the Netherlands Cancer Institute (an estimated 550 pts.). Under SA #2, Improvements in acute esophagitis NTCP models, we will: (a) accrue more patients (from 166 to an estimated 450 in 4 years), (b) incorporate new factors such as partial-circumferential irradiation and other metrics based on the shape of the high dose region, and (c) test and refine our model using new data contributed by the Netherlands Cancer Institute (an estimated 300 pts.). Under SA #3, Improvements in post-RT parotid salivary function/xerostomia models, we will: (a) test the effect of spatial placement of high-dose regions, (b) use the model to analyze the radio-protective effect of Amifostine on salivary function in an ongoing intensity modulated radiation therapy trial, and (c) test/refine our model against the University of Michigan xerostomia dataset. In addition, we will establish publicly archived databases with convenient and freely available software tools. We hypothesize that this research will result in a significantly improved ability to predict, on an individualized basis, the risk of xerostomia, pneumonitis, or esophagitis, and could thereby lead to improved radiation therapy treatments.