Collaborative Research: TMRP - Dynamic Data-Driven System for Laser Treatment of Cancer

合作研究：TMRP - 用于癌症激光治疗的动态数据驱动系统

• 批准号: 0540063
• 负责人: John Hazle
• 金额: $ 25.41万
• 依托单位: University of Texas, M.D. Anderson Cancer Center
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-10-01 至 2008-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0540063&HistoricalAwards=false
• 关键词: Collaborative; Research; TMRP; Dynamic; Data

The goal of this project is to develop a dynamic data-driven planning and controlsystem for laser treatment of cancer. The proposed research includes (1) development of a general mathematical framework and a family of mathematical and computational models of bio-heat transfer, tissue damage, and tumor viability, (2) dynamic calibration, verification and validation processes based on laboratory and clinical data and simulated response, and (3) design of effective thermo-therapeutic protocols using model predictions. At the core of the proposed systems is the adaptive-feedback control of mathematical and computational models based on a posteriori estimates of errors in key quantities of interest, and modern Magnetic Resonance Temperature Imaging (MRTI) and diode laser devices to monitor treatment of tumors in laboratory animals. This approach enables an automated systematic model selection process based on acceptance criteria determined a priori and is valid for models of events occurring at multiple spatial and temporal scales. The proposed project should be of interest to both NIH/NLM and NSF. The methodologies to be implemented involve uncertainty quantification methods designed to provide an innovative, data-driven, patient-specific approach to effective cancer treatment. The general mathematical framework resulting from this research will be applicable to any thermo-therapeutic cancer treatment, but our treatment protocols will be established based on tumors seeded in prostates of canines. The primary objective of the proposed research is to develop treatment strategies by selecting optimal parameter sets (such as laser power, wave length, and fluence rate) based on high fidelity model predictions and data from cellular and in vivo biological measurement, and MRTI thermal distributions.

本项目的目标是开发一个动态数据驱动的计划和控制系统，用于激光治疗癌症。拟议的研究包括（1）开发一个通用的数学框架和一系列生物热传递，组织损伤和肿瘤活力的数学和计算模型，（2）基于实验室和临床数据以及模拟响应的动态校准，验证和确认过程，以及（3）使用模型预测设计有效的热治疗方案。所提出的系统的核心是数学和计算模型的自适应反馈控制，该模型基于对感兴趣的关键量的误差的后验估计，以及现代磁共振温度成像（MRTI）和二极管激光设备，以监测实验室动物肿瘤的治疗。这种方法使一个自动化的系统模型选择过程的基础上确定的先验验收标准，并在多个空间和时间尺度上发生的事件的模型是有效的。建议的项目应该是感兴趣的NIH/NLM和NSF。要实施的方法涉及不确定性量化方法，旨在提供一种创新的、数据驱动的、针对患者的有效癌症治疗方法。从这项研究中得到的一般数学框架将适用于任何热疗癌症治疗，但我们的治疗方案将基于犬前列腺中的肿瘤。拟议的研究的主要目标是通过选择最佳参数集（如激光功率，波长和注量率）的基础上高保真模型预测和数据，从细胞和体内生物测量，和MRTI的热分布，制定治疗策略。