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Besides, advanced neuroimaging is increasingly used to non\u2010invasively provide detailed insights into the molecular and metabolic features of meningiomas. These techniques are usually based on MRI, e.g., perfusion\u2010weighted imaging, diffusion\u2010weighted 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\u2010genetic profile. Here, we review several diagnostic applications and future directions of these advanced neuroimaging techniques, including radiomics in preclinical models and patients with meningioma.<\/jats:p>","DOI":"10.1111\/bpa.13015","type":"journal-article","created":{"date-parts":[[2022,2,25]],"date-time":"2022-02-25T10:33:33Z","timestamp":1645785213000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Use of advanced neuroimaging and artificial intelligence in meningiomas"],"prefix":"10.1111","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2485-1796","authenticated-orcid":false,"given":"Norbert","family":"Galldiks","sequence":"first","affiliation":[{"name":"Department of Neurology Faculty of Medicine University Hospital Cologne University of Cologne  Cologne Germany"},{"name":"Institute of Neuroscience and Medicine (INM\u20103, \u20104) Research Center Juelich  Juelich Germany"},{"name":"Center for 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