Development of a Computational Paradigm for Laser Treatment of Cancer

Keywords:medical, laser,  Time:15-03-2016
Effective cancer treatment requires complete destruction of cancerous cells while maintaining functionality of infected organs. When cancerous tumors occur in well-defined and non-vital regions, conventional surgical procedures for removal of affected tissue is the customary medical treatment; but, traditionally, little can be done when tumors are small and spread over the region. In contrast, laser surgery is minimally invasive and simple to perform, potentially decreasing complications and minimizing hospitalization. Laser therapies provide a lethal doseofheatto thedesiredsitewhileminimizing damagetosurroundingtissue.In particular, laser-induced hyperthermia therapies promise effective treatment of small, poorly-defined metastases or other tumors embedded within vital regions. The success of laser treatments would be highly increased with the ability to provide a reliable prediction of treatment outcome at the time of treatment delivery using high fidelity computer predictions. Knowledge of temperature history versus time during treatment has been used to predict thermal necrosis in regions where damage is severe, but in regions where temperatures are insuffcient to coagulate proteins, the results and subsequent effects have been diffcult to predict. This is due, in part, to the expression of heat shock protein (HSP) in the regions of thermal stress, which provide enhanced viability of tumor tissue resulting in the recurrence of cancer. Consequently, knowledge of the thermal dose necessary to activate or de-activate HSP expression as a function of temperature and time in the affected tissue [5,4] can be critical in planning and implementing an effective thermal treatment by laser surgery. Grid-computing-enabled dynamic data-driven planning and control systems can provide a unique opportunity for conformal delivery of heat generated by diode or other types of lasers to the target. Image guided thermal ablation therapy surgery or as a complementary therapy for cancer management. In addition, image guidance has the potential to provide real-time treatment monitoring by providing temperature and thermal dose feedback during treatment delivery [7]. By including Magnetic Resonance Temperature Imaging (MRTI), the thermal dose delivered to surrounding normal tissue during therapy can be limited and a more conformal treatment achieved.

2 Description of Laser Treatment Aren

The primary objective of this paper is to describe an approach for guiding laser therapy of cancer, particularly prostate cancer, by accurate control and monitoring of the treatment process through computer simulation. This will be made possiblethroughthedevelopmentofdynamicdata-driven,high-fidelitycomputer simulation models correlated with in vivo spatiotemporal temperature information generated during hyperthermia, and cellular and in vivo HSP expression and damage data collected to adaptively control thermo-therapy of cancerous tumors. The specific aims supporting this objective are:

1. To develop an adaptive control system that operates over a computational grid connecting a Treatment/Measurement Arena (TMA) in Houston at the UT’s M.D. Anderson Cancer Center (MDACC) and a Computational/ Simulation Arena (CSA) in Austin at The University of Texas at Austin (UT Austin).

2. To develop new algorithms, laboratory and modeling protocols to enable the development of the control systems, including adaptive modeling and meshing procedures, calibration procedures, verification and validation procedures,inversemodeling and sensitivityanalysis algorithms,and laboratory proceduresfor measuringtissue damageand HSP expressionsto characterize the kinetic relationship in terms of temperature and time.

3. To demonstrate the effectiveness of the entire process by applying it to the treatment of actual prostate tumors in canines, using modern MRTI-guided laser surgery, distributed visualization and imaging techniques, and data storage and processing devices.