ReviewThe British journal of radiology2011
Conventional MRI evaluation of gliomas.
Review in The British journal of radiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 papers, 3 of them syntheses that pooled it.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
119 citing papers in PubMed, 3 syntheses or guidelines pooled it, 238 citations in OpenAlex.
- A systematic review of extracellular vesicles as non-invasive biomarkers in glioma diagnosis, prognosis, and treatment response monitoring.Molecular biology reports · 2021Pooled it
- MRI combined with PET-CT of different tracers to improve the accuracy of glioma diagnosis: a systematic review and meta-analysis.Neurosurgical review · 2019Pooled it
- Quantitative magnetic resonance imaging and radiogenomic biomarkers for glioma characterisation: a systematic review.The British journal of radiology · 2018Pooled it
- Contrast-enhancement in supratentorial low-grade gliomas: a classic prognostic factor in the molecular age.Journal of neuro-oncology · 2019Trial
- C11 Methionine PET (MET-PET) Imaging of Glioblastoma for Detecting Postoperative Residual Disease and Response to Chemoradiation Therapy.International journal of radiation oncology, biology, physics · 2018Trial
- A dual-discriminator conditional generative adversarial network (DDcGAN) approach to glioma grade classification with structural MRI images fusion.Physical and engineering sciences in medicine · 2026Article
- Role of CXCR4 PET Imaging for the Evaluation of Gliomas: A Comprehensive Literature Review.Cancers · 2026Review
- Preoperative tumor cerebral blood flow measured by arterial spin labeling predicts enhancing tumor volume progression in gliomas: a single-center retrospective study.BMC medical imaging · 2026Article
- Diagnostic and Prognostic Value of Hypoxia PET in Glioma: A Systematic Review and Meta-Analysis.Cancers · 2026Review
- Role of [18F]FMISO PET Imaging for the Evaluation of Gliomas: A Comprehensive Literature Review.Diagnostics (Basel, Switzerland) · 2026Review
- Malignant transformation in IDH-mutant gliomas: clinical drivers and survival outcomes in a histologically defined cohort.Journal of neuro-oncology · 2026Article
- Radiographic Enhancing Progression Without Molecular or Histologic Progression in IDH-Mutant Grade 2 Astrocytoma Treated With an IDH Inhibitor: A Case Report.Case reports in neurological medicine · 2026Article
- Diffuse glioma perfusion quantification with ASL and DSC: a head-to-head comparison withFrontiers in oncology · 2026Article
- Voxel-accurate MRI-microscopy Correlation Enables AI-powered Prediction of Brain Disease States.Theranostics · 2026Article
- CMRA-DETR: a lightweight and high-accuracy detection framework for MRI-based brain tumor identification.Frontiers in medicine · 2026Article
- Progressive axonal degeneration in white matter pathways traversing peritumoral penumbra in frontotemporal glioma.Computational diffusion MRI. CDMRI (Workshop) · 2026Article
- Preoperative peripheral blood routine indicators as predictors of prognosis and diagnosis in glioma under the 2021 World Health Organization classification.Oncology letters · 2025Article
- Review
- Histopathological and molecular characteristics of IDH-wildtype glioblastoma without contrast enhancement: Implications for clinical outcomes.Neuro-oncology · 2025Article
- Enhanced glioma semantic segmentation using U-net and pre-trained backbone U-net architectures.Scientific reports · 2025Article
59 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MRI using T(1) weighted, T(2) weighted and gadolinium-enhanced sequences plays a central clinical role in diagnosis, characterisation, surveillance and therapeutic monitoring of gliomas. Such conventional MRI protocols provide high resolution multiplanar structural information, and substantially improved tissue characterisation compared with CT. However, the MRI signal lacks biological specificity, e.g. T(2) weighted dependent signal abnormality is dominated by tissue water content, and contrast enhancement reflects a non-specific increase in blood-brain barrier permeability. This limits non-invasive glioma diagnosis, characterisation and therapeutic planning and assessment of active tumour load may be confounded by treatment-related effects. The complex features of glioma morphology and often subtle changes between MRI examinations are also frequently difficult to detect reliably by visual inspection of the images, even by an experienced radiologist. Moreover, the most widely used response criteria in clinical practice and therapeutic trials rely on linear measurements of enhancing tumour and are further challenged by the irregular shape and heterogeneous composition of gliomas. This contributes to the poor correlation of these criteria with hard clinical endpoints. While conventional MRI is widely available and provides essential anatomical information, the lack of pathology-specific biomarkers available from standard MRI sequences and methods of image analysis used limit overall diagnostic and prognostic efficacy of the examination.
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Registered trials
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.