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The permeability of tumor stroma (k [m^2]) is a measure of flow conductivity, and plays an important role in modeling tumor interstitial fluid flow (TIF). However, reliable tumor- and/or patient-specific k values are not available. Therefore, we devised a novel ex-vivo setup that allows measuring the permeability in clinical tissue samples. Additionally, we developed a computational model to better understand the effect of tumor tissue permeability on TIF. Fresh human tumor samples were harvested, sliced, and punched into circular discs, after which they were placed on the enclosure of modified Ussing diffusion chambers. Subsequently, k measurements were performed using two different diffusion chambers connected to a bubble tracker system to quantify the fluid exchange through the tissue induced by a hydrostatic pressure. In addition, a computational fluid dynamics (CFD) modeling approach was developed to investigate the sensitivity of TIF to the magnitude of k. The results of the measurements demonstrated a large difference between the permeability of various tumor types (range of 5E-18 to 4E-16 m²). More importantly, the results also demonstrated the heterogeneity of k values in single tumors, with k values that varied up to a factor of 4 in certain tumor types (e.g. between 5E-18 to 2E-17 m² for a peritoneal metastasis sample). The CFD model also showed that the heterogeneity of k can significantly affect the TIF, indicating the importance of personalized estimations of 3D spatial k profiles. A new experimental and computational method was successfully introduced to measure tumor stroma permeability values of human cancer tissue samples and model their impact on TIF. The results showed significant inter- and intratumor differences in k values and TIF, implying k values to be an important piece of the puzzle for personalized modeling of perfusion and treatment of solid tumors.

Since the development of CT-imaging in the 20th century, medical imaging has transformed the field of preoperative surgical planning. Today, 3D reconstructions of patient-specific anatomies are increasingly being used as input for computational models of various treatments. These treatment models can assist the clinician in comparing different treatment scenarios preoperatively and choosing the most optimal one for each patient-specific situation, thereby increasing health outcomes. Specifically, for liver cancer, we have developed a computational fluid dynamics (CFD) model in Fluent (Ansys, USA) of blood (ρ: 1060 kg/m^3) and drug particle (ρ: 1060 kg/m^3, diameter: 40 µm) flow in the hepatic arteries perfusing the tumor, based on the conebeam CT images of a patient with primary liver cancer treated at UZ Leuven. This CFD model allows us to predict the impact of various interventional parameters on the tumor dose distribution. For example, a microcatheter (length: 80 mm, diameter: 0.7 mm) positioned near the wall led to a much more uniform dose distribution than a microcatheter positioned at the center of the vessel. However, the total dose delivered to the tumor was only 43.2% for the off-center catheter, and 49.3% for the central position. These results show that different microcatheter positions can have a significant impact on both tumor dose and tumor dose distribution. Success stories of preoperative planning software companies like HeartFlow and FEops have shown the incredible potential of computational models in medicine. However, many of these computational models, including our own, need to be validated for a wide range of patients first before being integrated into clinical practice. Additionally, high computational complexity is another factor that might hamper clinical integration. In the near future, as medical imaging has done in the previous century, computational modelling might be the next big step in transforming the field of preoperative surgical planning.

Peritoneal metastasis (PM) is a type of cancer spread in which the peritoneum is affected by numerous tumor nodules. Pressurized intraperitoneal aerosol chemotherapy (PIPAC) is an innovative drug delivery method developed to treat PM using laparoscopy. During PIPAC treatment, chemotherapeutics are nebulized as aerosol droplets using a high-pressure injector (Injektron™ 82 M, Medtron, Germany) and an atomizer (Capnopen®, Germany) with the aim to obtain a homogeneous droplet distribution throughout the peritoneal cavity, thereby increasing the effectiveness of intraperitoneal chemotherapy. The goal of the present work was to investigate the effect of additional electrostatic precipitation (ePIPAC) on the aerosol distribution during PIPAC in a realistically reconstructed human peritoneal cavity using computational fluid dynamics (CFD) modeling.

International audience; Magnetic resonance navigation (MRN) has become a significant method to steer magnetized particles in medical applications by using MRI scanners. Dedicated MRN sequences have been successfully developed to track and steer such particle aggregates for selective chemoembolization of liver tumors. Particle aggregation is however a complex process and a comprehensive study of its shape and motion is needed to avoid damaging healthy tissues. Previous computational studies have considered non-aggregating particles or aggregates in 2D simplified settings. In this study, we investigate the forces acting on such micro-particle aggregation including drag, gravity, pressure gradient, virtual mass, magnetic gradient, dipole and contact forces. The code is developed in the OpenFOAM framework. Primary results are compared with bifurcation in vitro experimental data. Different sizes of clusters, directions of the magnetic field and gradient can be investigated to optimize aggregation form. This research represents an initial step towards complex in-vitro phantoms and in-vivo models.