ArticleFrontiers in oncology2022
Predicting the grade of meningiomas by clinical-radiological features: A comparison of precontrast and postcontrast MRI.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Heterogeneity, Measurement, and Clinical Implications of Oxygenation, Cell Signaling, and Redox Biology in Glioblastoma and Adult Diffuse Gliomas, with Context from Other Brain Tumors.Antioxidants (Basel, Switzerland) · 2026Review
- Radiographic predictors of peritumoral brain edema in intracranial meningiomas: a review of current controversies and illustrative cases.Chinese neurosurgical journal · 2024Review
- Review
- Domestic Animal Models of Central Nervous System Tumors: Focus on Meningiomas.Life (Basel, Switzerland) · 2023Review
- Article
- Association of quantitative radiomic shape features with functional outcome after surgery for primary sporadic dorsal spinal meningiomas.Frontiers in surgery · 2023Article
- Imaging meningioma biology: Machine learning predicts integrated risk score in WHO grade 2/3 meningioma.Neuro-oncology advancesArticle
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Authors and funding
8 authors.
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Abstract
Objectives: Postcontrast magnetic resonance imaging (MRI) is important for the differentiation between low-grade (WHO I) and high-grade (WHO II/III) meningiomas. However, nephrogenic systemic fibrosis and cerebral gadolinium deposition are major concerns for postcontrast MRI. This study aimed to develop and validate an accessible risk-scoring model for this differential diagnosis using the clinical characteristics and radiological features of precontrast MRI. Methods: From January 2019 to October 2021, a total of 231 meningioma patients (development cohort n = 137, low grade/high grade, 85/52; external validation cohort n = 94, low-grade/high-grade, 60/34) were retrospectively included. Fourteen types of demographic and radiological characteristics were evaluated by logistic regression analyses in the development cohort. The selected characteristics were applied to develop two distinguishing models using nomograms, based on full MRI and precontrast MRI. Their distinguishing performances were validated and compared using the external validation cohort. Results: One demographic characteristic (male), three precontrast MRI features (intratumoral cystic changes, lobulated and irregular shape, and peritumoral edema), and one postcontrast MRI feature (absence of a dural tail sign) were independent predictive factors for high-grade meningiomas. The area under the receiver operating characteristic (ROC) curve (AUC) values of the two distinguishing models (precontrast-postcontrast nomogram vs. precontrast nomogram) in the development cohort were 0.919 and 0.898 and in the validation cohort were 0.922 and 0.878. DeLong's test showed no statistical difference between the AUC values of the two distinguishing models ( Conclusions: An accessible risk-scoring model based on the demographic characteristics and radiological features of precontrast MRI is sufficient to distinguish between low-grade and high-grade meningiomas, with a performance equal to that of a full MRI, based on radiological features.
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