ArticleFrontiers in oncology2021
Diagnosis of Invasive Meningioma Based on Brain-Tumor Interface Radiomics Features on Brain MR Images: A Multicenter Study.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Role of the Neutrophil to Lymphocyte Ratio in Guillain Barré Syndrome: A Systematic Review and Meta-Analysis.Mediators of inflammation · 2022Pooled it
- 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
- A Novel Nomogram for Predicting Meningioma Grade Based on Radiomics Features and Clinical Characteristics.Current medical science · 2026Article
- A systematic review of multiparametric magnetic resonance imaging radiomics in the prediction of brain invasion in meningioma.Translational cancer research · 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
- 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
- Multiparameter MRI-based radiomics nomogram for preoperative prediction of brain invasion in atypical meningioma:a multicentre study.BMC medical imaging · 2024Article
- MRI-based radiomics signature: a potential imaging biomarker for prediction of microvascular invasion in combined hepatocellular-cholangiocarcinoma.Abdominal radiology (New York) · 2024Article
- MCE: Medical Cognition Embedded in 3D MRI feature extraction for advancing glioma staging.PloS one · 2024Article
- Applications and Integration of Radiomics for Skull Base Oncology.Advances in experimental medicine and biology · 2024Review
- A magnetic resonance imaging based radiomics model to predict mitosis cycles in intracranial meningioma.Scientific reports · 2023Article
- Normalization Strategies in Multi-Center Radiomics Abdominal MRI: Systematic Review and Meta-Analyses.IEEE open journal of engineering in medicine and biology · 2023Article
- Meningiomas with CNS invasion.Frontiers in neuroscience · 2023Review
- Preoperative surgical risk assessment of meningiomas: a narrative review based on MRI radiomics.Neurosurgical review · 2022Review
- The efficacy of preoperative MRI features in the diagnosis of meningioma WHO grade and brain invasion.Frontiers in oncology · 2022Article
- Base MRI Imaging Characteristics of Meningioma Patients to Discuss the WHO Classification of Brain Invasion Otherwise Benign Meningiomas.Technology in cancer research & treatmentArticle
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12 authors.
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Abstract
backgroundMeningioma invasion can be preoperatively recognized by radiomics features, which significantly contributes to treatment decision-making. Here, we aimed to evaluate the comparative performance of radiomics signatures derived from varying regions of interests (ROIs) in predicting BI and ascertaining the optimal width of the peritumoral regions needed for accurate analysis.
methodsFive hundred and five patients from Wuhan Union Hospital (internal cohort) and 214 cases from Taihe Hospital (external validation cohort) pathologically diagnosed as meningioma were included in our study. Feature selection was performed from 1,015 radiomics features respectively obtained from nine different ROIs (brain-tumor interface (BTI)2-5mm; whole tumor; the amalgamation of the two regions) on contrast-enhanced T1-weighted imaging using least-absolute shrinkage and selection operator and random forest. Principal component analysis with varimax rotation was employed for feature reduction. Receiver operator curve was utilized for assessing discrimination of the classifier. Furthermore, clinical index was used to detect the predictive power.
resultsModel obtained from BTI4mm ROI has the maximum AUC in the training set (0.891 (0.85, 0.932)), internal validation set (0.851 (0.743, 0.96)), and external validation set (0.881 (0.833, 0.928)) and displayed statistically significant results between nine radiomics models. The most predictive radiomics features are almost entirely generated from GLCM and GLDM statistics. The addition of PEV to radiomics features (BTI4mm) enhanced model discrimination of invasive meningiomas.
conclusionsThe combined model (radiomics classifier with BTI4mm ROI + PEV) had greater diagnostic performance than other models and its clinical application may positively contribute to the management of meningioma patients.
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