Computational Modeling of Pancreatic Cancer: Fluid Flow and Plasmonic Photothermal Therapy




Manrique-Bedoya, Santiago

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Pancreatic cancer has one of the highest mortality rates among all cancers, with a five-year survival of less than 10%. The development and progression of this disease is still largely misunderstood as a result of late-stage diagnosis in most patients, and the effectiveness of standard treatments is highly variable. Many patients are not surgical candidates, so novel approaches to deliver minimally invasive therapy are needed. Recent development of endoscopic technologies for pancreatic tumor ablation have shown promise in reducing tumor burden. One of these emerging technologies is plasmonic photothermal therapy (PPTT) which uses gold nanoparticles as heating agents to selectively ablate tumorous tissue.

Studying the impact of disease progression and treatment in both healthy and tumorous tissue requires in vivo experimentation, often carried out in mice, which results in a costly and timeconsuming experimental setup. Modern computational power can be used to simulate these experiments to reduce risk, time, and cost but requires knowledge of pancreatic tissue properties not currently characterized in the literature. By accurately assessing the physical properties of pancreatic tissue and utilizing anatomical data, we can develop a computational model to facilitate a better understanding of disease progression and treatment optimization.

Utilizing published data, we created an anatomically accurate computational model of the pancreas to analyze the effect of tumor growth in the pancreatic duct using fluid dynamics. We then developed a multiphysics computational model of heat therapy to study the effect of nanoparticle heating due to plasmonic excitation induced by laser illumination. Finally, we obtained tissue properties of pancreas from our ex vivo experiments to help build a multiphysics model of laser-tissue interaction.

By solving Navier-Stokes equations in the fluid dynamics model, we found that intraductal pressure in the pancreas did not dramatically increase until the pancreatic duct reached 80% blockage of its lumen. This result could help explain late stage symptoms of the disease. The PPTT model was used to compare the temperature change induced by gold nanorods (GNRs), nano bipyramids (GBPs), and nanospheres (GNSs) of different sizes by solving Maxwell’s equations and coupling the resulting solution to the heat equation. We found that gold nanorods of 91 nm in length absorbed the highest amount of energy and exhibited the greatest temperature rise, which makes them ideal candidates to be used as photothermal agents for PPTT. Locally concentrated GNRs could be delivered by endoscopic injection which would enhance the clustering effects and enable higher temperature increase within the target region. We succesfully measured thermal conductivity and light transmissivity of ex vivo pancreatic tissue to obtain more realistic results from the laser-tissue interaction model and inform future mathematical models of the pancreas.

These simulations can help optimize the current treatment modalities for pancreatic cancer, giving us the opportunity to intervene and improve survival using non-surgical techniques.


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computational modeling, intraductal pressure, pancreatic cancer, plasmonic heating, plasmonic photothermal therapy



Mechanical Engineering