Use of Advanced Neuroimaging and Artificial Intelligence in Meningiomas
Overview
Authors
Affiliations
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.
Artificial Intelligence, Radiomics, and Computational Modeling in Skull Base Surgery.
Suero Molina E, Ieva A Adv Exp Med Biol. 2024; 1462:265-283.
PMID: 39523271 DOI: 10.1007/978-3-031-64892-2_16.
Artificial intelligence in neuroimaging: Opportunities and ethical challenges.
Brahma N, Vimal S Brain Spine. 2024; 4:102919.
PMID: 39281849 PMC: 11399650. DOI: 10.1016/j.bas.2024.102919.
Radiomics-Based Prediction of TERT Promotor Mutations in Intracranial High-Grade Meningiomas.
Akkurt B, Spille D, Peetz-Dienhart S, Kiolbassa N, Mawrin C, Musigmann M Cancers (Basel). 2023; 15(17).
PMID: 37686690 PMC: 10486806. DOI: 10.3390/cancers15174415.
Wu H, Beylerli O, Gareev I, Beilerli A, Ilyasova T, Talybov R Oncol Lett. 2023; 26(2):350.
PMID: 37427340 PMC: 10326821. DOI: 10.3892/ol.2023.13936.
Radiomic and clinical nomogram for cognitive impairment prediction in Wilson's disease.
Tian L, Dong T, Hu S, Zhao C, Yu G, Hu H Front Neurol. 2023; 14:1131968.
PMID: 37188313 PMC: 10177658. DOI: 10.3389/fneur.2023.1131968.