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 …

Meningioma: not always a benign tumor. A review of advances in the treatment of meningiomas

I Maggio, E Franceschi, A Tosoni, VD Nunno… - CNS …, 2021 - Taylor & Francis
Meningiomas are the most common primary intracranial tumors. The majority of
meningiomas are benign, but they can present different grades of dedifferentiation from …

Prognostic impact of genetic alterations and methylation classes in meningioma

AS Berghoff, T Hielscher, G Ricken, J Furtner… - Brain …, 2022 - Wiley Online Library
Meningiomas are classified based on histological features, but genetic and epigenetic
features are emerging as relevant biomarkers for outcome prediction and may supplement …

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 …

Radiomics machine learning study with a small sample size: Single random training-test set split may lead to unreliable results

C An, YW Park, SS Ahn, K Han, H Kim, SK Lee - PLoS One, 2021 - journals.plos.org
This study aims to determine how randomly splitting a dataset into training and test sets
affects the estimated performance of a machine learning model and its gap from the test …

The diagnostic value of radiomics-based machine learning in predicting the grade of meningiomas using conventional magnetic resonance imaging: a preliminary …

C Chen, X Guo, J Wang, W Guo, X Ma, J Xu - Frontiers in oncology, 2019 - frontiersin.org
Objective: The purpose of the current study is to investigate whether texture analysis-based
machine learning algorithms could help devise a non-invasive imaging biomarker for …

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 …

Clinical significance of somatostatin receptor (SSTR) 2 in meningioma

W Wu, Y Zhou, Y Wang, L Liu, J Lou, Y Deng… - Frontiers in …, 2020 - frontiersin.org
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 …

[HTML][HTML] Meningioma

AA Alruwaili, O De Jesus - StatPearls [Internet], 2023 - ncbi.nlm.nih.gov
Objectives: Identify the etiology of meningioma. Describe the common physical signs seen in
patients with meningioma. Outline the management options for patients with meningioma …

A clinical semantic and radiomics nomogram for predicting brain invasion in WHO grade II meningioma based on tumor and tumor-to-brain interface features

N Li, Y Mo, C Huang, K Han, M He, X Wang… - Frontiers in …, 2021 - frontiersin.org
Background Brain invasion in meningioma has independent associations with increased
risks of tumor progression, lesion recurrence, and poor prognosis. Therefore, this study …