A Novel Metabolic-Based Method for Functional CT Imaging and Differentiation Between Cancer and Inflammation

One of the main limitations of the highly used cancer imaging technique, FDG-PET, is its inability to distinguish between cancerous lesions and post treatment inflammatory conditions, which are both characterized by increased glucose metabolic activity. To overcome this limitation, we developed a nanoparticle-based approach, utilizing glucose-functionalized gold nanoparticles (GF-GNPs) as a metabolically targeted CT contrast agent. Due to dissimilarities in vasculatures in different pathologic conditions, we hypothesized that the proposed technique may differentiate tumors from inflammation. Indeed, our approach has demonstrated superior abilities as compared to commonly used FDG-PET, in a combined tumor-inflammation mouse model. In addition, a comprehensive in vitro study with several cell types differing in their metabolic features, have demonstrated that the cellular uptake of GF-GNP strongly depends on GLUT1 surface expression, suggesting that our technology can be applicable to a wide range of cancers, characterized by high GLUT1-overexpression. In conclusion, our new concept of metabolic-based CT imaging overcomes the main drawbacks of the currently used FDG-PET and provides a new set of capabilities in cancer detection, staging and follow-up.