Meningioma: a review of epidemiology, pathology, diagnosis, treatment, and future directions
C Ogasawara, BD Philbrick, DC Adamson - Biomedicines, 2021 - mdpi.com
Meningiomas are the most common intracranial tumor, making up more than a third of all
primary central nervous system (CNS) tumors. They are mostly benign tumors that can be …
primary central nervous system (CNS) tumors. They are mostly benign tumors that can be …
Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis
Purpose To systematically review and evaluate the methodological quality of studies using
radiomics for diagnostic and predictive purposes in patients with intracranial meningioma …
radiomics for diagnostic and predictive purposes in patients with intracranial meningioma …
Detection of diagnostic and prognostic methylation-based signatures in liquid biopsy specimens from patients with meningiomas
Recurrence of meningiomas is unpredictable by current invasive methods based on
surgically removed specimens. Identification of patients likely to recur using noninvasive …
surgically removed specimens. Identification of patients likely to recur using noninvasive …
Loss of H3K27me3 in meningiomas
F Nassiri, JZ Wang, O Singh, S Karimi… - Neuro …, 2021 - academic.oup.com
Background There is a critical need for objective and reliable biomarkers of outcome in
meningiomas beyond WHO classification. Loss of H3K27me3 has been reported as a …
meningiomas beyond WHO classification. Loss of H3K27me3 has been reported as a …
Joint EANM/EANO/RANO/SNMMI practice guideline/procedure standards for diagnostics and therapy (theranostics) of meningiomas using radiolabeled somatostatin …
NL Albert, M Preusser, T Traub-Weidinger… - European Journal of …, 2024 - Springer
Purpose To provide practice guideline/procedure standards for diagnostics and therapy
(theranostics) of meningiomas using radiolabeled somatostatin receptor (SSTR) ligands …
(theranostics) of meningiomas using radiolabeled somatostatin receptor (SSTR) ligands …
Extensive peritumoral edema and brain-to-tumor interface MRI features enable prediction of brain invasion in meningioma: Development and validation
L Joo, JE Park, SY Park, SJ Nam, YH Kim… - Neuro …, 2021 - academic.oup.com
Background Brain invasion by meningioma is a stand-alone criterion for tumor atypia in the
2016 World Health Organization classification, but no imaging parameter has yet been …
2016 World Health Organization classification, but no imaging parameter has yet been …
Clinical significance of somatostatin receptor (SSTR) 2 in meningioma
Somatostatin receptor (SSTR) 2, widely expressed in meningioma, is a G-protein-coupled
receptor and can be activated by somatostatin or its synthetic analogs. SSTR2 is therefore …
receptor and can be activated by somatostatin or its synthetic analogs. SSTR2 is therefore …
A clinical semantic and radiomics nomogram for predicting brain invasion in WHO grade II meningioma based on tumor and tumor-to-brain interface features
Background Brain invasion in meningioma has independent associations with increased
risks of tumor progression, lesion recurrence, and poor prognosis. Therefore, this study …
risks of tumor progression, lesion recurrence, and poor prognosis. Therefore, this study …
A deep learning radiomics model may help to improve the prediction performance of preoperative grading in meningioma
L Yang, P Xu, Y Zhang, N Cui, M Wang, M Peng, C Gao… - Neuroradiology, 2022 - Springer
Purpose This study aimed to investigate the clinical usefulness of the enhanced-T1WI-based
deep learning radiomics model (DLRM) in differentiating low-and high-grade meningiomas …
deep learning radiomics model (DLRM) in differentiating low-and high-grade meningiomas …
Brain tumor segmentation (brats) challenge 2024: Meningioma radiotherapy planning automated segmentation
D LaBella, K Schumacher, M Mix, K Leu… - arXiv preprint arXiv …, 2024 - arxiv.org
The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT)
challenge aims to advance automated segmentation algorithms using the largest known …
challenge aims to advance automated segmentation algorithms using the largest known …