ArticleEBioMedicine2020
A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study.
Article in EBioMedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 66 papers, 2 of them syntheses that pooled it.
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Who cites it
66 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
- Machine learning for predicting post-operative outcomes in meningiomas: a systematic review and meta-analysis.Acta neurochirurgica · 2024Pooled it
- A Machine Learning Model Based on Unsupervised Clustering Multihabitat to Predict the Pathological Grading of Meningiomas.BioMed research international · 2022Trial
- 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
- Can brain-to-tumour outer interface radiomics improve the efficiency of MRI in predicting the brain invasion of meningiomas?Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Interpretable MRI Radiomics for Preoperative Meningioma Consistency Prediction.Journal of imaging informatics in medicine · 2026Article
- Integrating diffusion-weighted MRI radiomics features to predict brain invasion of meningiomas.Neurosurgical review · 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
- Deep learning radiomic nomogram outperforms the clinical model in distinguishing intracranial solitary fibrous tumors from angiomatous meningiomas and can predict patient prognosis.European radiology · 2025Article
- Preoperative diagnosis of meningioma sinus invasion based on MRI radiomics and deep learning: a multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Radiomics-driven neuro-fuzzy framework for rule generation to enhance explainability in MRI-based brain tumor segmentation.Frontiers in neuroinformatics · 2025Article
- Digital modeling of the gallbladder for revealing microanatomical features and optimizing surgical approaches in gallbladder-preserving cholelithotomy.Frontiers in physiology · 2025Article
- Peritumoral vessel characteristics on magnetic resonance venography reflect the sinus invasion status of para-sinus meningioma.Quantitative imaging in medicine and surgery · 2024Article
- Predicting long-term outcomes in patients with classical trigeminal neuralgia following microvascular decompression with an MRI-based radiomics nomogram: a multicentre study.European radiology · 2024Article
- Preoperative prediction of CNS WHO grade and tumour aggressiveness in intracranial meningioma based on radiomics and structured semantics.Scientific reports · 2024Article
- Prediction of prognosis in glioblastoma with radiomics features extracted by synthetic MRI images using cycle-consistent GAN.Physical and engineering sciences in medicine · 2024Article
- Non-invasively identifying candidates of active surveillance for prostate cancer using magnetic resonance imaging radiomics.Visual computing for industry, biomedicine, and art · 2024Article
- Multiparameter MRI-based radiomics nomogram for preoperative prediction of brain invasion in atypical meningioma:a multicentre study.BMC medical imaging · 2024Article
- MRI- and DWI-Based Radiomics Features for Preoperatively Predicting Meningioma Sinus Invasion.Journal of imaging informatics in medicine · 2024Article
6 more citing papers are in PubMed but not listed here.
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11 authors.
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Abstract
backgroundPrediction of brain invasion pre-operatively rather than postoperatively would contribute to the selection of surgical techniques, predicting meningioma grading and prognosis. Here, we aimed to predict the risk of brain invasion in meningioma pre-operatively using a nomogram by incorporating radiomic and clinical features.
methodsIn this case-control study, 1728 patients from Beijing Tiantan Hospital (training cohort: n = 1070) and Lanzhou University Second Hospital (external validation cohort: n = 658) were diagnosed with meningiomas by histopathology. Radiomic features were extracted from the T1-weighted post-contrast and T2-weighted magnetic resonance imaging. The least absolute shrinkage and selection operator was used to select the most informative features of different modalities. The support vector machine algorithm was used to predict the risk of brain invasion. Furthermore, a nomogram was constructed by incorporating radiomics signature and clinical risk factors, and decision curve analysis was used to validate the clinical usefulness of the nomogram.
findingsSixteen features were significantly correlated with brain invasion. The clinicoradiomic model derived from the fusing MRI sequences and sex resulted in the best discrimination ability for risk prediction of brain invasion, with areas under the curves (AUCs) of 0•857 (95% CI, 0•831-0•887) and 0•819 (95% CI, 0•775-0•863) and sensitivities of 72•8% and 90•1% in the training and validation cohorts, respectively.
interpretationOur clinicoradiomic model showed good performance and high sensitivity for risk prediction of brain invasion in meningioma, and can be applied in patients with meningiomas.
fundingThis work was supported by the National Natural Science Foundation of China (81772006, 81922040); the Youth Innovation Promotion Association CAS (grant numbers 2019136); special fund project for doctoral training program of Lanzhou University Second Hospital (grant numbers YJS-BD-33).
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