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

该项目的目标是开发一个动态数据驱动的癌症激光治疗计划和控制系统。建议的研究包括(1)建立一个通用的数学框架和一系列生物热传递、组织损伤和肿瘤生存能力的数学和计算模型；(2)基于实验室和临床数据和模拟反应的动态校准、验证和验证过程；(3)利用模型预测设计有效的热治疗方案。所提出系统的核心是基于关键量误差的后验估计的数学和计算模型的自适应反馈控制，以及用于监测实验动物肿瘤治疗的现代磁共振温度成像（MRTI）和二极管激光设备。这种方法能够基于先验确定的接受标准进行自动系统的模型选择过程，并且对在多个空间和时间尺度上发生的事件模型有效。提议的项目应引起NIH/NLM和NSF的兴趣。要实施的方法包括不确定性量化方法，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。本研究得出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建立在犬前列腺肿瘤的基础上。该研究的主要目标是根据高保真模型预测、细胞和体内生物测量数据以及MRTI热分布，通过选择最佳参数集（如激光功率、波长和通量）来制定治疗策略。

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.