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

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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.