ArticlePloS one2017
Radiographic prediction of meningioma grade by semantic and radiomic features.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers, 2 of them syntheses that pooled it.
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Who cites it
75 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance of Radiomics-based machine learning and deep learning-based methods in the prediction of tumor grade in meningioma: a systematic review and meta-analysis.Neurosurgical review · 2025Pooled it
- Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics.European journal of nuclear medicine and molecular imaging · 2019Pooled it
- MRI-based preoperative WHO grade prediction in meningioma: development and comparison of deep learning, radiomics, clinical, and combined models in multicenter data.Journal of neuro-oncology · 2026Article
- Early Prediction of Volumetric Progression in Sellar-Parasellar Meningiomas Using Delta Radiomics on 6-Month MRI Following Gamma Knife Radiosurgery.Diagnostics (Basel, Switzerland) · 2026Article
- A Radiomics-Driven Model to Distinguish Between Clinically Similar Myxopapillary Ependymomas and Lumbosacral Schwannomas.Neurosurgery practice · 2026Article
- A Novel Nomogram for Predicting Meningioma Grade Based on Radiomics Features and Clinical Characteristics.Current medical science · 2026Article
- Neuroimaging and machine learning fusion for improved brain tumor diagnosis and prognosis.Scientific reports · 2026Article
- Diagnostic Value of Routine MRI Parameters in Identifying High-Grade Intracranial Meningiomas.Neuroradiology · 2026Article
- MRI radiomic signature predicts peritumoral brain edema resolution following meningioma surgery.Acta neurochirurgica · 2025Article
- A systematic review of multiparametric magnetic resonance imaging radiomics in the prediction of brain invasion in meningioma.Translational cancer research · 2025Review
- Advancements in the application of MRI radiomics in meningioma.Radiation oncology (London, England) · 2025Review
- Meningioma: Molecular Updates from the 2021 World Health Organization Classification of CNS Tumors and Imaging Correlates.AJNR. American journal of neuroradiology · 2025Review
- Review
- Prediction of early recurrence of adult-type diffuse gliomas following radiotherapy using multi-modal magnetic resonance images.Medical physics · 2024Article
- A large scale multi institutional study for radiomics driven machine learning for meningioma grading.Scientific reports · 2024Article
- Preoperative prediction of CNS WHO grade and tumour aggressiveness in intracranial meningioma based on radiomics and structured semantics.Scientific reports · 2024Article
- Multiparameter MRI-based radiomics nomogram for preoperative prediction of brain invasion in atypical meningioma:a multicentre study.BMC medical imaging · 2024Article
- The development of a combined clinico-radiomics model for predicting post-operative recurrence in atypical meningiomas: a multicenter study.Journal of neuro-oncology · 2024Article
- Predicting Histologic Grade of Meningiomas Using a Combined Model of Radiomic and Clinical Imaging Features from Preoperative MRI.Biomedicines · 2023Article
- Predictors of recurrence after surgical resection of parafalcine and parasagittal meningiomas.Acta neurochirurgica · 2023Article
15 more citing papers are in PubMed but not listed here.
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Authors and funding
19 authors.
Funding
Abstract
objectivesThe clinical management of meningioma is guided by tumor grade and biological behavior. Currently, the assessment of tumor grade follows surgical resection and histopathologic review. Reliable techniques for pre-operative determination of tumor grade may enhance clinical decision-making.
methodsA total of 175 meningioma patients (103 low-grade and 72 high-grade) with pre-operative contrast-enhanced T1-MRI were included. Fifteen radiomic (quantitative) and 10 semantic (qualitative) features were applied to quantify the imaging phenotype. Area under the curve (AUC) and odd ratios (OR) were computed with multiple-hypothesis correction. Random-forest classifiers were developed and validated on an independent dataset (n = 44).
resultsTwelve radiographic features (eight radiomic and four semantic) were significantly associated with meningioma grade. High-grade tumors exhibited necrosis/hemorrhage (ORsem = 6.6, AUCrad = 0.62-0.68), intratumoral heterogeneity (ORsem = 7.9, AUCrad = 0.65), non-spherical shape (AUCrad = 0.61), and larger volumes (AUCrad = 0.69) compared to low-grade tumors. Radiomic and sematic classifiers could significantly predict meningioma grade (AUCsem = 0.76 and AUCrad = 0.78). Furthermore, combining them increased the classification power (AUCradio = 0.86). Clinical variables alone did not effectively predict tumor grade (AUCclin = 0.65) or show complementary value with imaging data (AUCcomb = 0.84).
conclusionsWe found a strong association between imaging features of meningioma and histopathologic grade, with ready application to clinical management. Combining qualitative and quantitative radiographic features significantly improved classification power.
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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.