Literature DB >> 28799683

Positron emission tomography imaging in gliomas: applications in clinical diagnosis, for assessment of prognosis and of treatment effects, and for detection of recurrences.

W-D Heiss1.   

Abstract

Neuroimaging plays a significant role in the diagnosis of intracranial tumours, especially brain gliomas, and must consist of an assessment of location and extent of the tumour and of its biological activity. Therefore, morphological imaging modalities and functional, metabolic or molecular imaging modalities should be combined for primary diagnosis and for following the course and evaluating therapeutic effects. Magnetic resonance imaging (MRI) is the gold standard for providing detailed morphological information and can supply some additional insights into metabolism (MR spectroscopy) and perfusion (perfusion-weighted imaging) but still has limitations in identifying tumour grade, invasive growth into neighbouring tissue and treatment-induced changes, as well as recurrences. These insights can be obtained by various positron emission tomography (PET) modalities, including imaging of glucose metabolism, amino acid uptake and nucleoside uptake. Diagnostic accuracy can benefit from coregistration of PET results and MRI, combining high-resolution morphological images with biological information. These procedures are optimized by the newly developed combination of PET and MRI modalities, permitting the simultaneous assessment of morphological, functional, metabolic and molecular information on the human brain.
© 2017 EAN.

Entities:  

Keywords:  brain tumours; functional imaging; gliomas; molecular imaging; positron emission tomography

Mesh:

Substances:

Year:  2017        PMID: 28799683     DOI: 10.1111/ene.13385

Source DB:  PubMed          Journal:  Eur J Neurol        ISSN: 1351-5101            Impact factor:   6.089


  3 in total

1.  Effect of blood glucose level on standardized uptake value (SUV) in 18F- FDG PET-scan: a systematic review and meta-analysis of 20,807 individual SUV measurements.

Authors:  Mahsa Eskian; Abass Alavi; MirHojjat Khorasanizadeh; Benjamin L Viglianti; Hans Jacobsson; Tara D Barwick; Alipasha Meysamie; Sun K Yi; Shingo Iwano; Bohdan Bybel; Federico Caobelli; Filippo Lococo; Joaquim Gea; Antonio Sancho-Muñoz; Jukka Schildt; Ebru Tatcı; Constantin Lapa; Georgia Keramida; Michael Peters; Raef R Boktor; Joemon John; Alexander G Pitman; Tomasz Mazurek; Nima Rezaei
Journal:  Eur J Nucl Med Mol Imaging       Date:  2018-10-22       Impact factor: 9.236

2.  Differentiation of Treatment-Related Effects from Glioma Recurrence Using Machine Learning Classifiers Based Upon Pre-and Post-Contrast T1WI and T2 FLAIR Subtraction Features: A Two-Center Study.

Authors:  Xin-Yi Gao; Yi-Da Wang; Shi-Man Wu; Wen-Ting Rui; De-Ning Ma; Yi Duan; An-Ni Zhang; Zhen-Wei Yao; Guang Yang; Yan-Ping Yu
Journal:  Cancer Manag Res       Date:  2020-05-07       Impact factor: 3.989

3.  Static FET PET radiomics for the differentiation of treatment-related changes from glioma progression.

Authors:  Marguerite Müller; Oliver Winz; Robin Gutsche; Ralph T H Leijenaar; Martin Kocher; Christoph Lerche; Christian P Filss; Gabriele Stoffels; Eike Steidl; Elke Hattingen; Joachim P Steinbach; Gabriele D Maurer; Alexander Heinzel; Norbert Galldiks; Felix M Mottaghy; Karl-Josef Langen; Philipp Lohmann
Journal:  J Neurooncol       Date:  2022-07-19       Impact factor: 4.506

  3 in total

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