Background: Nutrient and drug penetration into any solid tumor are critical determinants of the tumor’s response to treatment. They depend on both the density of microvasculature within the tumor microenvironment, as well as the exchange rates of nutrients between the microvasculature and the extracellular space. But these parameters are heterogenous, varying considerably from location to location within the tumor and surrounding tissues. The Toft’s model and its analogues date back to the early 1990s, and have been used to estimate vascular density, exchange rates, and extracellular-extravascular volume in a spatially-resolved manner using dynamic contrast enhaced (DCE) MRI’s. Unfortunately, accurately extracting kinetic parameters from a DCE time-series requires the images to have a time-resolution of just a few seconds, which is rarely done in clinical practice.
Methods: We employ a custom designed parallel algorithm to fit DCE MRI data to an exactly-solved ODE model of tissue perfusion kinetics.
Results: Here we describe a simplified model of tissue perfusion that can be fit to DCE time traces with temporal resolutions of 90 seconds or more. We show that for many breast tumors, the vascular density and tissue-vascular exchange rate are such that they give rise to a halo of fast-perfusing tissue on the tumor periphery, and slower-perfusing tissue inside. We then use this model as part of a more comprehensive tumor simulation methodology to predict how different patients will respond to neoadjuvant chemotherapy (NACT). We find that the incorporation of our microvascular model gives rise to significantly more accurate predictions of post-treatment tumor volume.
Conclusions: Performing perfusion kinetics analyses on clinical MRIs is both challenging, but critical for accurately predicting how a patient will respond to treatment. Our model, which relaxes the requirement for fine DCE temporal resolution, allows for these analyses to be performed on a larger swath of patients without the need for small volumes of interest, or ultra-fast MRI techniques. Moreover, when used within a broader tumor-modeling framework, our model increases the accuracy of predictions of tumor response to NACT.
