Numerical modeling in support of locoregional drug delivery during transarterial therapies for liver cancer

Abstract

During the past years, in silico medicine has grown more popular as an alternative to experimental techniques. This chapter overviews the state of the art of in silico modeling of the mass transport phenomena related to locoregional drug delivery in the hepatic arterial tree. The long-term goal is to use in silico models to improve the treatment of hepatocellular carcinoma (HCC), which is the most common form of primary liver cancer and one of the leading causes of cancer-related deaths worldwide. The preferred treatment for unresectable HCC is transarterial chemo- (TACE) or radioembolization (TARE). During these treatments, microparticles are injected in the hepatic arteries to selectively damage tumor tissue through embolization combined with chemotherapeutic effects (in case of TACE) or emission of high-intensity beta radiation (in case of TARE). Currently, a wide variability in clinical parameters exists to perform these therapies: different types of imaging modalities (e.g., MRI, conebeam CT), microcatheters (e.g., standard end-hole microcatheters, antireflux catheters), particles for radio- (e.g., SIR-Spheres) and chemoembolization (e.g., HepaSpheres), and injection conditions (e.g., injection location, velocity, catheter tip orientation). Moreover, the response to these therapies is highly heterogeneous, underlining the need to better understand the mass transport of the particles in the liver. Computational fluid dynamics (CFD) modeling is a useful tool to model the mass transport of drug particles (discrete phase) carried by the blood flow (fluid phase) in the liver. Previous CFD studies showed a large variety in workflow and modeling strategies. As such, hepatic arterial simulation geometries were most often literature-based or partially patient-inspired, while the use of patient-specific geometries was limited to only a few studies due to its complexity. Furthermore, modeling approaches differed substantially (e.g., modeling only the fluid phase or also the discrete particle phase, the degree of coupling between both phases, outlet boundary conditions implemented as pressure- vs flow-type, etc.), which significantly impacts the results. To date, results have shown that the choice of axial and cross-sectional injection locations can have a high impact on the particle distribution. Hence, it would be theoretically possible to steer particles toward specific outlets of the domain by optimizing the catheter tip location. Moreover, several studies have shown that the downstream particle distribution also depends on other parameters, including injection flow rate, microsphere size and density, etc., as is illustrated in this chapter. These findings demonstrate that injection conditions may be optimized depending on patient-specific conditions and that CFD techniques can assist in improving the treatment outcome. While validation is key to integrating in silico models into clinical practice, current validation is rather limited. In vitro techniques typically use 3D prints of the hepatic arterial geometry integrated into a perfusion circuit to mimic injection conditions and measure particle distributions. To date, simple and planar geometries have mostly been used, while patient-specific tortuous 3D geometries offer a more physiological alternative. While previous research has shown that a myriad of injection parameters can impact particle fate, further steps need to be taken to validate the CFD models and investigate the clinical potential of controlling injection parameters to increase therapy efficiency.