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）的重大挑战是呼吸诱导的肿瘤运动，这阻碍了足够的治疗剂量到靶量。尽管现代肿瘤运动管理策略用于正电子发射断层扫描/计算机断层扫描（PET/CT）引导的RT越来越多，但这些技术尚未完全纳入临床实践中。这主要是因为，由于呼吸模式的患者内和患者间的变异性高，并非每个患者都会受益于昂贵且冗长的运动管理的PET/CT扫描。该项目的目的是弥合知识差距哪种运动管理方法将最能使人受益。该项目将开发一个新的决策范式，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结合在一起，以预测每个患者的运动管理方法的好处。将基于我们现有的3,000多名患者的现有呼吸道痕迹数据库，将开发出一种决策分析队列模型，以比较和评估新决策范式的成本效益和基于人群的辐射肿瘤学实践。虽然专门用于围绕呼吸道运动管理的决策，但开发的决策范式可以被推广并应用于其他现实生活决策分析问题。该奖项支持数据挖掘/机器学习和决策分析中的基本研究，这将为有效管理患者特异性肿瘤运动的工具提供所需的知识。该项目中的建模工作将为1）建立一个新的数学基础，用于监督多元稀疏变量选择和预测，以发现高维变量之间复杂的多元关系； 2）构建一个一般的综合验证框架，以严格测试患者特定健康干预措施的成本效益。新的多元稀疏变量选择和预测方法可用于构建一个可解释的预测模型，处理具有较低样本量的高维数据，避免造成不足的效果，并结合结构化的组选择。成本效益分析框架整合了预测模型的结果，治疗效果和生存结果模型。该建模旨在定量估计长期的癌症生存结果，从选择性运动控制的患者特定辐射剂量计划中的改善。