ArticleBiomedicines2023
Predicting Histologic Grade of Meningiomas Using a Combined Model of Radiomic and Clinical Imaging Features from Preoperative MRI.
Article in Biomedicines, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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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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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.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- MRI radiomics in diagnosing high and low grade meningiomas through systematic review and meta analysis.Scientific reports · 2025Pooled 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
- Metastatic Meningioma: Neuroradiologic and Molecular Imaging Perspectives.Radiology. Imaging cancer · 2025Review
- Advancements in the application of MRI radiomics in meningioma.Radiation oncology (London, England) · 2025Review
- A Radiomic Model for Gliomas Grade and Patient Survival Prediction.Bioengineering (Basel, Switzerland) · 2025Article
- A large scale multi institutional study for radiomics driven machine learning for meningioma grading.Scientific reports · 2024Article
- Prediction of Histological Grade of Oral Squamous Cell Carcinoma Using Machine Learning Models Applied toBiomedicines · 2024Article
- Brain MRI Radiomic First-Order Features for Presurgical Prediction of Meningioma Grading.Journal of neuroimaging : official journal of the American Society of NeuroimagingArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Meningiomas are common primary brain tumors, and their accurate preoperative grading is crucial for treatment planning. This study aimed to evaluate the value of radiomics and clinical imaging features in predicting the histologic grade of meningiomas from preoperative MRI. We retrospectively reviewed patients with intracranial meningiomas from two hospitals. Preoperative MRIs were analyzed for tumor and edema volumes, enhancement patterns, margins, and tumor-brain interfaces. Radiomics features were extracted, and machine learning models were employed to predict meningioma grades. A total of 212 patients were included. In the training group (Hospital 1), significant differences were observed between low-grade and high-grade meningiomas in terms of tumor volume (
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Registered trials
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