Irreversible electroporation (IRE) is a focal ablation technique that has been shown in recent clinical trials to be effective in treating pancreatic cancer. The technique uses short, high voltage pulses to induce nanoscale pores in the target cell membranes, leading to cell death. Due to its non-thermal mechanism, IRE is particularly well suited for treating a tumor that is unresectable due to its close location to crucial structures such as blood vessels and nerves. Predicting the region of treatment is critical for optimal treatment of the tumor. The only predictive tools clinicians currently rely on for IRE treatment planning are computer tomography (CT), ultrasound (US) imaging, and real-time resistance measurement is used to monitor treatment progress. However, there is currently no method to plan optimal pulse parameters such as voltage, pulse duration, pulse number, and electrode spacing prior to treatment. Computational treatment planning models aim to perform this prediction in 3D, however, the electric field region relies on the electrical response of human tissue during IRE. This work quantifies this response for the first time and implements human tissue properties in a patient-specific, 3D treatment planning model. / Master of Science / Pancreatic cancer results in 40,000 deaths every year in the U.S, making it one of the most challenging diseases to treat. The current treatments for this disease fall short and have failed to significantly extend patient life expectancy. A technique called irreversible electroporation (IRE) has been shown in recent clinical trials to be effective in treating pancreatic cancer. IRE excels at treating tumors that are located near important blood vessels, nerves, and other important structures. However, clinicians do not have a way to visualize the region of treatment before surgery. In the research setting, 3D computational models aim to predict this area, but so far these models have been based on animal tissue, often of the incorrect organ type. This work applies IRE to human tissue samples, quantifies its electrical behavior, and implements that information in a personalized, predictive 3D model.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/83535 |
| Date | January 2018 |
| Creators | White, Natalie B. |
| Contributors | Electrical and Computer Engineering, Jia, Xiaoting, Safaai-Jazi, Ahmad, Davalos, Rafael V. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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