Collaborative Research: Decision Model for Patient-Specific Motion Management in Radiation Therapy Planning

协作研究：放射治疗计划中患者特定运动管理的决策模型

• 批准号: 1536407
• 负责人: Wanpracha Chaovalitwongse
• 金额: $ 18.48万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1536407&HistoricalAwards=false
• 关键词: Collaborative; Research; Decision; Model; Patient

A significant challenge in lung cancer radiation therapy (RT) is respiration-induced tumor motion, which hinders sufficient delivery of curative doses to target volumes. Although modern tumor motion management strategies for positron emission tomography/computed tomography (PET/CT)-guided RT are becoming more available, those techniques have yet to be fully incorporated into clinical practice. This is mainly because not every patient will benefit from a costly and lengthy motion-managed PET/CT scan due to high intra-patient and inter-patient variability of respiratory patterns. The objective of this project is to bridge the knowledge gap of which motion management method would best benefit an individual patient. This project will develop a new decision-making paradigm, in which machine learning techniques will be developed to characterize respiratory motion patterns and combine them with other diagnostic factors to predict the benefits from motion management methods for each individual patient. A decision-analytic cohort model will be developed to compare and evaluate the cost-effectiveness of the new decision paradigm and the traditional population-based radiation oncology practice of motion management based on our existing database of respiratory traces from more than 3,000 patients. While specifically applied to decisions surrounding respiratory motion management, the developed decision paradigm can be generalized and applied to other real life decision analysis problems.This award supports fundamental research in data mining/machine learning and decision analysis, which will provide needed knowledge for the development of tools for effective management of patient-specific tumor motion. The modeling effort in this project will 1) establish a new mathematical foundation for supervised multivariate sparse variable selection and prediction to discover complicated multivariate relationships among high-dimensional variables; 2) construct a general integrated validation framework to rigorously test the cost-effectiveness of patient-specific health interventions. The new multivariate sparse variable selection and prediction approach can be used to build an interpretable prediction model, handle high-dimensional data with a low sample size, avoid under-shrinkage effect, and incorporate structured group selection. The cost-effectiveness analysis framework integrates the outcome of prediction model, the treatment effect and survival outcome model. This modeling aims to quantitatively estimate long-term cancer survival outcomes from improvement in patient-specific planning of radiation dosing by selective motion control.

肺癌放射治疗(RT)的一个重要挑战是呼吸诱导的肿瘤运动，它阻碍了足够的治疗剂量输送到目标体积。虽然用于正电子发射断层扫描/计算机断层扫描(PET/CT)引导的放射治疗的现代肿瘤运动管理策略变得更加可行，但这些技术尚未完全应用于临床实践。这主要是因为，由于呼吸模式的患者内和患者间的高度可变性，并不是每个患者都能从昂贵而漫长的运动管理PET/CT扫描中受益。该项目的目标是弥合哪种运动管理方法对个体患者最有利的知识鸿沟。该项目将开发一种新的决策模式，其中将开发机器学习技术来表征呼吸运动模式，并将其与其他诊断因素相结合，以预测每个患者从运动管理方法中获得的好处。将开发一个决策分析队列模型，以比较和评估新的决策范例与传统的基于人群的放射肿瘤学运动管理实践的成本效益，该实践基于我们现有的3000多名患者的呼吸道痕迹数据库。该奖项支持数据挖掘/机器学习和决策分析方面的基础研究，这将为有效管理患者特定肿瘤运动的工具的开发提供必要的知识。该项目的建模工作将1)为有监督的多变量稀疏变量选择和预测建立新的数学基础，以发现高维变量之间的复杂多变量关系；2)构建一个通用的集成验证框架，以严格测试针对患者的卫生干预措施的成本-效果。新的多元稀疏变量选择和预测方法可用于建立可解释的预测模型，以低样本量处理高维数据，避免欠收缩效应，并纳入结构化分组选择。成本-效果分析框架综合了预测模型的结果、治疗效果和生存结果模型。该模型旨在通过选择性运动控制改善针对患者的放射剂量计划，从而定量评估癌症的长期生存结果。