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

doi:10.1021/acsnano.5b07576

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 journal › Article › peer-review

48 Scopus citations

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 language	English
Pages (from-to)	3469-3477
Number of pages	9
Journal	ACS Nano
Volume	10
Issue number	3
DOIs	https://doi.org/10.1021/acsnano.5b07576
State	Published - 22 Mar 2016

Bibliographical note

Funding Information:
This work was partially supported by the Research and Development Chief Scientist Kamin grant (49544), by the Israel Cancer Research Fund (ICRF), by the Israel Science Foundation grant (749/14), and by the doctoral scholarship for applicable and scientific engineering research, granted to Tamar Dreifuss and Oshra Betzer by the Ministry of Science, Technology & Space, Israel.

Publisher Copyright:
© 2016 American Chemical Society.

Keywords

CT
FDG-PET
cancer
gold nanoparticles
metabolic-based imaging

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1021/acsnano.5b07576

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

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.",

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AU - Panet, Hana

AU - Popovtzer, Aron

AU - Santana, Jordan

AU - Abourbeh, Galith

AU - Mishani, Eyal

AU - Popovtzer, Rachela

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AB - 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.

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

Abstract

Bibliographical note

Keywords

UN SDGs

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