ArticleAnnals of medicine and surgery (2012)2022
Role of Magnetic Resonance Imaging (MRI) in grading gliomas comparable with pathology: A cross-sectional study from Syria.
Article in Annals of medicine and surgery (2012), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
11 citing papers in PubMed, 18 citations in OpenAlex.
- Head-to-head comparison of ⁶⁸Ga-FAPI and ¹⁸F-FDG PET/CT in intracranial space-occupying lesions.Annals of nuclear medicine · 2026Article
- Multi-Omics Integration for Advancing Glioma Precision Medicine.Annals of clinical and translational neurology · 2026Review
- Extra-Axial Cerebellopontine Angle Low Grade Glioma - Unusual Occurrence in a 43-year-old Woman.Pakistan journal of medical sciences · 2025Article
- Development of a Fully Optimized Convolutional Neural Network for Astrocytoma Classification in MRI Using Explainable Artificial Intelligence.Journal of imaging · 2025Article
- Oxidative Stress and Antioxidants in Glioblastoma: Mechanisms of Action, Therapeutic Effects and Future Directions.Antioxidants (Basel, Switzerland) · 2025Review
- T1-contrast enhanced MRI generation from multi-parametric MRI for glioma patients with latent tumor conditioning.Medical physics · 2025Article
- Radiogenomics and machine learning predict oncogenic signaling pathways in glioblastoma.Journal of translational medicine · 2025Article
- Scoping review of image-based overall survival prediction in glioma using machine learning.Polish journal of radiology · 2025Review
- Diagnostic accuracy of MRI in detecting early brain tumors: A comparative study with CT scan.Bioinformation · 2025Article
- Understanding highand low-grade gliomas: VASARI criteria and MRI features.Radiologia brasileiraArticle
- Evaluation of glial tumors: correlation between magnetic resonance imaging and histopathological analysis.Radiologia brasileiraArticle
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Gliomas are one of the most common brain tumors in adults with a poor prognosis in most patients. Magnetic Resonance Imaging (MRI) plays a critical role in the diagnosis, management, and follow-up of gliomas. The aim of this study is to assess the sensitivity and specificity of MRI in the preoperative grading of supratentorial gliomas in comparison to histopathology. Methods: A cross-sectional study included 39 patients, aged between 40 and 75 years with histologically diagnosed supratentorial gliomas who underwent conventional MR imaging, which included T1, T2, and FLAIR sequences from November 2018-December 2019 in the Department of Neurosurgery, Tishreen University Hospital, Lattakia. The histopathological typing and grading of the tumor were done by using 2016 WHO classification. The sensitivity, specificity, predictive value, and accuracy of MRI in determining tumor grade were calculated. The comparison was done between MRI findings and WHO histopathological grading. Results: The overall sensitivity and specificity of MRI findings in the assessment of high-grade gliomas were 100% and 91% respectively. The positive predictive value (PPV) was 66.6%, and the negative predictive value (NPV) was 100%. The overall accuracy was 94.9%. The agreement between histopathological and MRI findings was 72%. Conclusions: MRI plays an essential role in the initial diagnosis and grading of supratentorial gliomas with high sensitivity and specificity. It is considered a non-invasive method and is useful in cases where the biopsy procedure is a contraindication or rejected by the patient.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.