TY  - JOUR
AU  - Galldiks, Norbert
AU  - Angenstein, Frank
AU  - Werner, Jan-Michael
AU  - Bauer, Elena K
AU  - Gutsche, Robin
AU  - Fink, Gereon R
AU  - Langen, Karl-Josef
AU  - Lohmann, Philipp
TI  - Use of advanced neuroimaging and artificial intelligence in meningiomas.
JO  - Brain pathology
VL  - 32
IS  - 2
SN  - 1750-3639
CY  - Oxford
PB  - Wiley-Blackwell
M1  - DZNE-2022-00023
SP  - e13015
PY  - 2022
N1  - (CC BY)
AB  - 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.
KW  - Artificial Intelligence
KW  - Humans
KW  - Meningeal Neoplasms: diagnostic imaging
KW  - Meningioma: diagnostic imaging
KW  - Neuroimaging
KW  - Positron-Emission Tomography
KW  - MRI (Other)
KW  - PET (Other)
KW  - radiogenomics (Other)
KW  - radiomics (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:35213083
C2  - pmc:PMC8877736
DO  - DOI:10.1111/bpa.13015
UR  - https://pub.dzne.de/record/163188
ER  -