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: 1537504
• 负责人: Shouyi Wang
• 金额: $ 6.52万
• 依托单位: University of Texas at Arlington
• 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 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1537504&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）的一个重大挑战是呼吸诱导的肿瘤运动，这阻碍了治疗剂量充分输送到靶体积。尽管用于正电子发射断层扫描/计算机断层扫描（PET/CT）引导的RT的现代肿瘤运动管理策略变得越来越可用，但这些技术尚未完全纳入临床实践。这主要是因为，由于呼吸模式的患者内和患者间变异性高，并非每个患者都将受益于昂贵且冗长的运动管理PET/CT扫描。本项目的目的是弥合知识差距的运动管理方法将最有利于个别病人。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联合收割机相结合，以预测运动管理方法对每个患者的益处。将开发一个决策分析队列模型，根据我们现有的3,000多名患者的呼吸轨迹数据库，比较和评估新决策范式和传统的基于人群的放射肿瘤学运动管理实践的成本效益。虽然专门应用于呼吸运动管理的决策，开发的决策范式可以推广和应用到其他真实的生活决策分析problem.This奖支持数据挖掘/机器学习和决策分析的基础研究，这将提供必要的知识，为有效管理患者特定的肿瘤运动的工具的开发。该项目的建模工作将：1）为监督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复杂的多变量关系; 2）构建一个通用的综合验证框架，以严格测试患者特定健康干预措施的成本效益。新的多变量稀疏变量选择和预测方法可用于构建可解释的预测模型，处理高维数据，低样本量，避免欠收缩效应，并结合结构化组选择。成本效果分析框架整合了结果预测模型、治疗效果和生存结果模型。该建模旨在通过选择性运动控制改善患者特定的放射剂量规划来定量估计长期癌症生存结果。