Differentiating between Cancer and Inflammation: A Metabolic-Based Method for Functional Computed Tomography Imaging

Menachem Motiei, Tamar Dreifuss, Oshra Betzer, Hana Panet, Aron Popovtzer, Jordan Santana, Galith Abourbeh, Eyal Mishani, Rachela Popovtzer

Research output: Contribution to journalArticlepeer-review

Abstract

One of the main limitations of the highly used cancer imaging technique, PET-CT, is its inability to distinguish between cancerous lesions and post treatment inflammatory conditions. The reason for this lack of specificity is that [18F]FDG-PET is based on increased glucose metabolic activity, which characterizes both cancerous tissues and inflammatory cells. To overcome this limitation, we developed a nanoparticle-based approach, utilizing glucose-functionalized gold nanoparticles (GF-GNPs) as a metabolically targeted CT contrast agent. Our approach demonstrates specific tumor targeting and has successfully distinguished between cancer and inflammatory processes in a combined tumor-inflammation mouse model, due to dissimilarities in angiogenesis occurring under different pathologic conditions. This study provides a set of capabilities in cancer detection, staging and follow-up, and can be applicable to a wide range of cancers that exhibit high metabolic activity.

Original languageEnglish
Pages (from-to)3469-3477
Number of pages9
JournalACS Nano
Volume10
Issue number3
DOIs
StatePublished - 22 Mar 2016
Externally publishedYes

Keywords

  • cancer
  • CT
  • FDG-PET
  • gold nanoparticles
  • metabolic-based imaging

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