Administration of chemotherapy forms one of the cornerstones of cancer treatment. Chemotherapy may be applied locoregionally in an organ or tissue. Intraperitoneal drug delivery (IPDD) is an established treatment regimen in patients with peritoneal carcinomatosis from colorectal, appendiceal, and ovarian cancer. Cancer of the pancreas is characterized by a very rigid stroma, with abundant production of hyaluronic acid (HA), resulting in an elevated Interstitial Fluid Pressure (IFP) and very poor drug penetration. Functional MRI techniques such as Dynamic Contrast Enhanced MRI and MR Elastography have shown great potential for non-invasive estimation of IFP.
Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) offers the possibility to measure interstitial fluid velocity after bolus injection of a Gd based contrast agent. Combined with computational fluid dynamics (CFD) modeling, dynamic MRI data can be used to generate detailed 3D maps of the tissue IFP distribution. Magnetic Resonance Elastography (MRE) is a non-invasive technique to measure tissue stiffness in vivo. Recently, a a proof-of-concept method to infer tumour pressure noninvasively using stiffness measurements from MRE and the application of nonlinear biomechanical models was published (1).
In an ongoing study at UZ Gent & UGent both DCE-MRI and MRE are obtained in patients suffering from pancreatic cancer. The goal of this project is to reconstruct IFP distributions from MRE and DCE. You will implement the proof of concept method, integrate the MR elastography data obtained in this project, and combine it with the CFD modeling results created with DCE-MRI input. You will work in a multidisciplinary team from the depts. of gastrointestinal surgery, radiology, and biomedical engineering. This way, we will take a next step forward towards non-invasive IFP estimation in patients.
1. Fovargue D, Fiorito M, Capilnasiu A, Nordsletten D, Lee J, Sinkus R: Towards noninvasive estimation of tumour pressure by utilising MR elastography and nonlinear biomechanical models: a simulation and phantom study. Scientific Reports 2020; 10:5588.