Gaussian mixture model-based classification of dynamic contrast enhanced MRI data for identifying diverse tumor microenvironments: preliminary results

Abstract

Tumor hypoxia develops heterogeneously, affects radiation sensitivity and the development of metastases. Prognostic information derived from the in vivo characterization of the spatial distribution of hypoxic areas in solid tumors can be of value for radiation therapy planning and for monitoring the early treatment response. Tumor hypoxia is caused by an imbalance between the supply and consumption of oxygen. The tumor oxygen supply is inherently linked to its vasculature and perfusion which can be evaluated by dynamic contrast enhanced (DCE-) MRI using the contrast agent Gd-DTPA. Thus, we hypothesize that DCE-MRI data may provide surrogate information regarding tumor hypoxia. In this study, DCE-MRI data from a rat prostate tumor model were analysed with a Gaussian mixture model (GMM)-based classification to identify perfused, hypoxic and necrotic areas for a total of ten tumor slices from six rats, of which one slice was used as training data for GMM classifications. The results of pattern recognition analyzes were validated by comparison to corresponding Akep maps defining the perfused area (0.84 +/- 0.09 overlap), hematoxylin and eosin (H&E)-stained tissue sections defining necrosis (0.64 +/- 0.15 overlap) and pimonidazole-stained sections defining hypoxia (0.72 +/- 0.17 overlap), respectively. Our preliminary data indicate the feasibility of a GMM-based classification to identify tumor hypoxia, necrosis and perfusion/permeability from non-invasively acquired, in vivo DCE-MRI data alone, possibly obviating the need for invasive procedures, such as biopsies, or exposure to radioactivity, such as positron emission tomography (PET) exams.