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001     163188
005     20240320115516.0
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100 1 _ |a Galldiks, Norbert
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245 _ _ |a Use of advanced neuroimaging and artificial intelligence in meningiomas.
260 _ _ |a Oxford
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520 _ _ |a Anatomical cross-sectional imaging methods such as contrast-enhanced MRI and CT are the standard for the delineation, treatment planning, and follow-up of patients with meningioma. Besides, advanced neuroimaging is increasingly used to non-invasively provide detailed insights into the molecular and metabolic features of meningiomas. These techniques are usually based on MRI, e.g., perfusion-weighted imaging, diffusion-weighted imaging, MR spectroscopy, and positron emission tomography. Furthermore, artificial intelligence methods such as radiomics offer the potential to extract quantitative imaging features from routinely acquired anatomical MRI and CT scans and advanced imaging techniques. This allows the linking of imaging phenotypes to meningioma characteristics, e.g., the molecular-genetic profile. Here, we review several diagnostic applications and future directions of these advanced neuroimaging techniques, including radiomics in preclinical models and patients with meningioma.
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650 _ 7 |a MRI
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650 _ 7 |a PET
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650 _ 7 |a radiogenomics
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650 _ 7 |a radiomics
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650 _ 2 |a Artificial Intelligence
|2 MeSH
650 _ 2 |a Humans
|2 MeSH
650 _ 2 |a Meningeal Neoplasms: diagnostic imaging
|2 MeSH
650 _ 2 |a Meningioma: diagnostic imaging
|2 MeSH
650 _ 2 |a Neuroimaging
|2 MeSH
650 _ 2 |a Positron-Emission Tomography
|2 MeSH
700 1 _ |a Angenstein, Frank
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700 1 _ |a Werner, Jan-Michael
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700 1 _ |a Bauer, Elena K
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700 1 _ |a Gutsche, Robin
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700 1 _ |a Fink, Gereon R
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700 1 _ |a Langen, Karl-Josef
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700 1 _ |a Lohmann, Philipp
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773 _ _ |a 10.1111/bpa.13015
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910 1 _ |a Deutsches Zentrum für Neurodegenerative Erkrankungen
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