ArticleNeuro-oncology2021
Extensive peritumoral edema and brain-to-tumor interface MRI features enable prediction of brain invasion in meningioma: development and validation.
Article in Neuro-oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 2 of them syntheses that pooled it.
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Who cites it
58 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Radiomics-based artificial intelligence models in brain tumors: A systematic review and meta-analysis of diagnostic performance.Neuroradiology · 2026Pooled it
- Peritumoral T2/FLAIR hyperintense MRI findings of meningiomas are not necessarily edema and may persist permanently: a systematic review.Neurosurgical review · 2023Pooled 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
- 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
- FOXC1 as a molecular predictor of postoperative peritumoral edema resolution in meningiomas.Journal of neuro-oncology · 2026Article
- Unlocking the full potential of MRI with artificial intelligence for pre-operative meningioma evaluation.European radiology · 2026Article
- Deficiency of SMARCB1 drives an immunosuppressive microenvironment in meningioma.Acta neuropathologica communications · 2026Article
- Non-invasive preoperative MRI-based deep transfer learning radiomics model for predicting meningioma brain invasion.Frontiers in oncology · 2026Article
- Preoperative prediction of brain invasion in meningiomas: a comparison of diffusion kurtosis imaging and diffusion tensor imaging.BMC neurology · 2025Article
- A systematic review of multiparametric magnetic resonance imaging radiomics in the prediction of brain invasion in meningioma.Translational cancer research · 2025Review
- Single-cell transcriptomic analysis reveals metastatic and immunosuppressive characteristics in meningioma brain-tumor interface.Journal of translational medicine · 2025Article
- Advancements in the application of MRI radiomics in meningioma.Radiation oncology (London, England) · 2025Review
- Exploring a recurrence model for atypical meningioma based on multiparametric MRI radiomic and clinical characteristics: a multicenter retrospective cohort study.Radiation oncology (London, England) · 2025Article
- Radiomics based on brain-to-tumor interface enables prediction of metastatic tumor type of brain metastasis: a proof-of-concept study.La Radiologia medica · 2025Article
- Deep learning on brain metastasis for predicting EGFR genotype and EGFR-TKI therapy response in metastatic NSCLC: a multicenter study.Frontiers in bioengineering and biotechnology · 2025Article
- Gut-Brain-Microbiome Axis in the Regulation of Cancer Immune Escape and Immunotherapy in Tumors.Research (Washington, D.C.) · 2025Review
- MRI-based multiregional radiomics for preoperative prediction of Ki-67 expression in meningiomas: a two-center study.Frontiers in neurology · 2025Article
- FLAIR-based radiomics signature from brain-tumor interface for early prediction of response to EGFR-TKI therapy in NSCLC patients with brain metastasis.Frontiers in cell and developmental biology · 2025Article
- New imaging findings of tumor invasion into brain tissue: "Peritumoral Hyperintense Enhancement Sign".Frontiers in human neuroscience · 2025Article
- Establishment of an MRI-based radiomics model for distinguishing between intramedullary spinal cord tumor and tumefactive demyelinating lesion.BMC medical imaging · 2024Article
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7 authors.
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Abstract
backgroundBrain invasion by meningioma is a stand-alone criterion for tumor atypia in the 2016 World Health Organization classification, but no imaging parameter has yet been shown to be sufficient for predicting it. The aim of this study was to develop and validate an MRI-based radiomics model from the brain-to-tumor interface to predict brain invasion by meningioma.
methodsPreoperative T2-weighted and contrast-enhanced T1-weighted imaging data were obtained from 454 patients (88 patients with brain invasion) between 2012 and 2017. Feature selection was performed from 3222 radiomics features obtained in the 1 cm thickness tumor-to-brain interface region using least absolute shrinkage and selection operator. Peritumoral edema volume, age, sex, and selected radiomics features were used to construct a random forest classifier-based diagnostic model. The performance was evaluated using the areas under the curves (AUCs) of the receiver operating characteristic in an independent cohort of 150 patients (29 patients with brain invasion) between 2018 and 2019.
resultsVolume of peritumoral edema was an independent predictor of brain invasion (P < 0.001). The top 6 interface radiomics features plus the volume of peritumoral edema were selected for model construction. The combined model showed the highest performance for prediction of brain invasion in the training (AUC 0.97; 95% CI: 0.95-0.98) and validation sets (AUC 0.91; 95% CI: 0.84-0.98), and improved diagnostic performance over volume of peritumoral edema only (AUC 0.76; 95% CI: 0.66-0.86).
conclusionAn imaging-based model combining interface radiomics and peritumoral edema can help to predict brain invasion by meningioma and improve the diagnostic performance of known clinical and imaging parameters.
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