SynthesisNeurosurgical review2018
Can MRI predict meningioma consistency?: a correlation with tumor pathology and systematic review.
Synthesis in Neurosurgical review, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed, 86 citations in OpenAlex.
- Correlation of Preoperative Diffusion-weighted Magnetic Resonance Imaging-based Virtual Elastography with Intraoperative Quantitative Tumor Stiffness in Meningiomas.Neurologia medico-chirurgica · 2026Article
- Interpretable MRI Radiomics for Preoperative Meningioma Consistency Prediction.Journal of imaging informatics in medicine · 2026Article
- Relaxometry assessment of meningioma consistency using quantitative magnetic resonance imaging.Neuroradiology · 2026Article
- Preoperative prediction of meningioma consistency using tumor-to-cerebellar peduncle intensity ratios on T2WI, DWI, and ADC maps.Medicine · 2026Observational
- Reproducible meningioma grading across multi-center MRI protocols via hybrid radiomic and deep learning features.Neuroradiology · 2025Article
- Metabolomics-based liquid biopsy reflects molecular alterations induced by meningioma.Discover oncology · 2025Article
- Multimodal deep learning-based radiomics for meningioma consistency prediction: integrating T1 and T2 MRI in a multi-center study.BMC medical imaging · 2025Article
- Predicting intraoperative meningioma consistency using features from standard MRI sequences: a preoperative evaluation.Acta neurochirurgica · 2025Article
- Predicting critical surgical characteristics of intracranial meningiomas on MRI-A prospective study on 100 consecutive patients.Neuro-oncology practice · 2025Article
- Article
- Preoperative Assessment of Meningioma Consistency Using a Combination of MR Elastography and DTI.AJNR. American journal of neuroradiology · 2024Article
- Automated volumetry of meningiomas in contrast-enhanced T1-Weighted MRI using deep learning.World neurosurgery: X · 2024Article
- Clinical Significance of Stiffness during Endoscopic Surgery for Intracerebral Hemorrhage: A Retrospective Study.Neurologia medico-chirurgica · 2023Article
- Performance Test of a Well-Trained Model for Meningioma Segmentation in Health Care Centers: Secondary Analysis Based on Four Retrospective Multicenter Data Sets.Journal of medical Internet research · 2023Article
- A rare case of multiple supratentorial brain lesions due to meningiomatosis.Radiology case reports · 2023Article
- Relationship Between Quantitative Tumor Consistency and Pathological Factors in Intracranial Meningioma.Acta neurochirurgica · 2023Article
- Intelligent noninvasive meningioma grading with a fully automatic segmentation using interpretable multiparametric deep learning.European radiology · 2023Article
- Sclerosing meningioma with a large peritumoral cyst: Case report.Radiology case reports · 2023Article
- The Party Wall: Redefining the Indications of Transcranial Approaches for Giant Pituitary Adenomas in Endoscopic Era.Cancers · 2023Review
- Meningioma microstructure assessed by diffusion MRI: An investigation of the source of mean diffusivity and fractional anisotropy by quantitative histology.NeuroImage. Clinical · 2023Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Tumor consistency is a critical factor that influences operative strategy and patient counseling. Magnetic resonance imaging (MRI) describes the concentration of water within living tissues and as such, is hypothesized to predict aspects of their biomechanical behavior. In meningiomas, MRI signal intensity has been used to predict the consistency of the tumor and its histopathological subtype, though its predictive capacity is debated in the literature. We performed a systematic review of the PubMed database since 1990 concerning MRI appearance and tumor consistency to assess whether or not MRI can be used reliably to predict tumor firmness. The inclusion criteria were case series and clinical studies that described attempts to correlate preoperative MRI findings with tumor consistency. The relationship between the pre-operative imaging characteristics, intraoperative findings, and World Health Organization (WHO) histopathological subtype is described. While T2 signal intensity and MR elastography provide a useful predictive measure of tumor consistency, other techniques have not been validated. T1-weighted imaging was not found to offer any diagnostic or predictive value. A quantitative assessment of T2 signal intensity more reliably predicts consistency than inherently variable qualitative analyses. Preoperative knowledge of tumor firmness affords the neurosurgeon substantial benefit when planning surgical techniques. Based upon our review of the literature, we currently recommend the use of T2-weighted MRI for predicting consistency, which has been shown to correlate well with analysis of tumor histological subtype. Development of standard measures of tumor consistency, standard MRI quantification metrics, and further exploration of MRI technique may improve the predictive ability of neuroimaging for meningiomas.
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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.