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

肺癌放射治疗（RT）的一个重大挑战是呼吸诱导的肿瘤运动，这阻碍了治疗剂量足够的输送到靶体积。虽然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，由于患者内部和患者之间呼吸模式的高度可变性，并不是每个患者都能从昂贵且长时间的运动管理PET/CT扫描中受益。该项目的目的是弥合运动管理方法对个体患者最有利的知识差距。该项目将开发一种新的决策范式，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相结合，以预测每个患者运动管理方法的益处。基于我们现有的3000多名患者的呼吸痕迹数据库，我们将开发一个决策分析队列模型，以比较和评估新的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。虽然专门应用于围绕呼吸运动管理的决策，但开发的决策范式可以推广并应用于其他现实生活中的决策分析问题。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，这将为开发有效管理患者特异性肿瘤运动的工具提供所需的知识。本课题的建模工作将：(1)为有监督的多元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元关系；2)构建综合验证框架，严格检验针对患者的卫生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以建立可解释的预测模型，处理低样本量的高维数据，避免欠缩效应，并结合结构化的群体选择。成本-效果分析框架整合了预测结果模型、治疗效果模型和生存结果模型。该模型旨在定量估计通过选择性运动控制改善患者特异性放射剂量计划的长期癌症生存结果。

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.