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

59 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

Publisher Copyright:
© 2016 American Chemical Society.

Funding

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.

Funders	Funder number
Research and Development Chief Scientist Kamin	49544
Israel Cancer Research Fund
Ministry of Science, Technology and Space
Israel Science Foundation	749/14

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

Cite this

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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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KW - FDG-PET

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

Abstract

Bibliographical note

Funding

Keywords

UN SDGs

Access to Document

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