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- W2999649225 abstract "ABSTRACT The change in paradigm towards individualized, tailored therapy has led to a need for diagnosing at the molecular level. Most of the molecular biology methods used today need tissue sampling for in vitro analysis. In contrast, molecular imaging allows for non-invasive studies at the molecular level in living, intact organisms. With PET it is possible to label bio-molecules with radioactive isotopes. This method can be used for non-invasive visualization of tumor specific receptors and tissue characteristics such as receptor expression, angiogenesis and ability to metastasize. Especially within cancer biology the technique is expected to lead to a break-through in diagnosing and treatment. Among the different techniques for molecular imaging, the nuclear medicine based technologies have the greatest potential for translational use since methods developed in animal models may directly be transferred and used in humans. Furthermore, PET has a high sensitivity and allows for quantification. The lecture will review the current state of molecular imaging of NET with focus on the use of PET. Areas of recent developments include several new somatostatin receptor PET ligands that has improved diagnostic accuracy compared with previous methods using SPECT. In addition, FDG has been shown as a valuable prognostic tool and recent data from our institution will be shown. Finally, several new PET tracers seem valuable for tailoring and monitoring effect of new targeted therapies. Examples of such tailoring and monitoring of therapy will be reviewed and it will be discussed what the future clinical role in NET of such PET tracers may be. Disclosure The author has declared no conflicts of interest." @default.
- W2999649225 created "2020-01-23" @default.
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- W2999649225 date "2012-09-01" @default.
- W2999649225 modified "2023-09-29" @default.
- W2999649225 title "Molecular Imaging of Nets" @default.
- W2999649225 doi "https://doi.org/10.1016/s0923-7534(20)32688-0" @default.
- W2999649225 hasPublicationYear "2012" @default.
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