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000906460 1001_ $$0P:(DE-Juel1)143792$$aGalldiks, Norbert$$b0$$eCorresponding author
000906460 245__ $$aUse of advanced neuroimaging and artificial intelligence in meningiomas
000906460 260__ $$aOxford$$bWiley-Blackwell$$c2022
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000906460 520__ $$aAnatomical cross-sectional imaging methods such as contrast-enhanced MRI and CT are the standard for the delineation, treatment planning, and follow-up of patients with meningioma. Besides, advanced neuroimaging is increasingly used to non-invasively provide detailed insights into the molecular and metabolic features of meningiomas. These techniques are usually based on MRI, e.g., perfusion-weighted imaging, diffusion-weighted imaging, MR spectroscopy, and positron emission tomography. Furthermore, artificial intelligence methods such as radiomics offer the potential to extract quantitative imaging features from routinely acquired anatomical MRI and CT scans and advanced imaging techniques. This allows the linking of imaging phenotypes to meningioma characteristics, e.g., the molecular-genetic profile. Here, we review several diagnostic applications and future directions of these advanced neuroimaging techniques, including radiomics in preclinical models and patients with meningioma.
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000906460 7001_ $$0P:(DE-HGF)0$$aAngenstein, Frank$$b1
000906460 7001_ $$0P:(DE-HGF)0$$aWerner, Jan-Michael$$b2
000906460 7001_ $$0P:(DE-HGF)0$$aBauer, Elena K.$$b3
000906460 7001_ $$0P:(DE-Juel1)181076$$aGutsche, Robin$$b4
000906460 7001_ $$0P:(DE-Juel1)131720$$aFink, Gereon R.$$b5
000906460 7001_ $$0P:(DE-Juel1)131777$$aLangen, Karl-Josef$$b6
000906460 7001_ $$0P:(DE-Juel1)145110$$aLohmann, Philipp$$b7$$eCorresponding author
000906460 773__ $$0PERI:(DE-600)2029927-8$$a10.1111/bpa.13015$$gVol. 32, no. 2$$n2$$p-$$tBrain pathology$$v32$$x1015-6305$$y2022
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