Normal Tissue Complication Modeling for Radiotherapy

放射治疗的正常组织并发症建模

• 批准号: 7913472
• 负责人: Joseph O Deasy
• 金额: $ 11.37万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2010-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7913472
• 关键词: 3-Dimensional; Acute; Adult; Amifostine; Applications Grants; Archives; Area; Award; Bioinformatics; Biological Models; Budgets; Clinic; Clinical Trials; Collaborations; Communities; Complication; Computer software; Congresses; Data; Data Set; Databases; Deglutition; Disease; Doctor of Philosophy; Dose; Environment; Esophagitis; Esophagus; Fibrosis; Functional disorder; Goals; Grant; Head and neck structure; Heart; Image; Informatics; Institutes; Institution; Intensity-Modulated Radiotherapy; Japan; Journals; Lead; Letters; Logistic Regressions; Lung; Malignant Neoplasms; Methods; Metric; Michigan; Modeling; Netherlands; Non-linear Models; Normal tissue morphology; Outcome; Paper; Parotid Gland; Patients; Pneumonia; Positron-Emission Tomography; Probability; Publications; Publishing; Pulmonary function tests; Radiation; Radiation Oncology; Radiation Pneumonitis; Radiation Therapy Oncology Group; Radiation therapy; Radio; Research; Research Personnel; Review Committee; Risk; Risk Factors; Salivary; Science; Shapes; Software Tools; Study models; Sweden; System; Techniques; Testing; Toxic effect; Universities; Work; Xerostomia; abstracting; base; data mining; design; disease characteristic; improved; irradiation; meetings; novel; open source; predictive modeling; protective effect; software systems; tool; treatment planning; tumor

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.

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体积、患者和疾病特征，为头颈部和肺部治疗并发症制作改进的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3D治疗计划的数据库，包括来自多个机构的数据集。使用由此获得的数据，我们将使用多指标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。将采用自助法检验变量选择的稳健性。对于肺部治疗计划，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，RT后晚期肺炎/纤维化NTCP模型的改进下，我们将：（a）扩展当前可用的清洗。大学数据集（来自166名患者）4年内估计为450例），（B）研究纳入新因素，如空间变化敏感性和治疗前肺功能试验，（c）使用RTOG 93-11数据集（113例患者）测试和改进我们的模型，以及（d）根据杜克大学和荷兰癌症研究所提供的数据（估计550例患者）测试和改进我们的模型。根据SA #2，急性食管炎NTCP模型的改进，我们将：（a）增加病人数目（4年内从166例增加到估计的450例），（B）结合新的因素，如部分圆周照射和基于高剂量区域形状的其他指标，以及（c）使用荷兰癌症研究所提供的新数据（估计300例患者）测试和改进我们的模型。根据SA #3，RT后腮腺唾液功能/口干模型的改善，我们将：（a）测试高剂量区域空间放置的影响，（B）使用该模型分析正在进行的调强放疗试验中氨磷汀对唾液功能的辐射保护作用，以及（c）根据密歇根大学口干数据集测试/完善我们的模型。此外，我们还将利用方便和免费提供的软件工具建立公开存档的数据库。我们假设这项研究将导致在个体化基础上显著提高预测口干症、肺炎或食管炎风险的能力，从而改善放射治疗。