Use of advanced neuroimaging and artificial intelligence in meningiomas.
Norbert GalldiksFrank AngensteinJan-Michael WernerElena K BauerRobin GutscheGereon R FinkKarl-Josef LangenPhilipp LohmannPublished in: Brain pathology (Zurich, Switzerland) (2022)
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.
Keyphrases
- contrast enhanced
- diffusion weighted imaging
- computed tomography
- diffusion weighted
- artificial intelligence
- magnetic resonance imaging
- high resolution
- magnetic resonance
- positron emission tomography
- machine learning
- dual energy
- cross sectional
- big data
- deep learning
- photodynamic therapy
- stem cells
- image quality
- gene expression
- mass spectrometry
- climate change
- mesenchymal stem cells
- optic nerve
- bone marrow
- network analysis