ArticleJournal of neuroimaging : official journal of the American Society of Neuroimaging
Brain MRI Radiomic First-Order Features for Presurgical Prediction of Meningioma Grading.
Article in Journal of neuroimaging : official journal of the American Society of Neuroimaging. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Deep Learning for Automated Detection and Segmentation of Meningioma on Multiparametric MRI.Journal of imaging informatics in medicine · 2026Article
- Brain MRI Radiomic First-Order Features for Presurgical Prediction of Meningioma Grading.Journal of neuroimaging : official journal of the American Society of NeuroimagingArticle
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14 authors.
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Abstract
background and purposeGrading meningioma guides treatment choices from follow-up to surgical resection with adjuvant radiation. Radiomics may offer a non-invasive alternative to biopsies. We assessed radiomic features (RFs) for distinguishing Grade 1 and Grade 2 meningiomas on preoperative multiparametric MRI.
methodsPresurgical T1-weighted (T1), T2-weighted (T2), T2 gradient echo-weighted (T2GRE), fluid-attenuated inversion recovery (FLAIR), apparent diffusion coefficient (ADC), and T1-weighted contrast-enhanced (T1CE). MRI sequences of histopathologically diagnosed meningiomas were collected retrospectively. Each volume had 75 RFs extracted from semimanually segmented tumors using MintLesion Research (Version 3.10). The Lasso method selected variables from imputed data, and 10-fold cross-validation determined the optimal regularization parameter. For Lasso-retained variables, multivariate effects were estimated.
resultsOut of 150 patients (67.3% women), 110 (73.3%) had Grade 1 meningiomas, and 40 (26.7%) Grade 2. The strongest metrics to distinguish meningiomas Grade 1 versus Grade 2 were intensity histogram coefficient of variation on T1CE (odds ratio [OR] 0.47, 95% confidence interval [CI] 0.23-0.88; p = 0.028), maximum histogram gradient on T1 (OR 2.11, 95% CI 1.18-4.82; p = 0.043), and intensity histogram quartile coefficient of dispersion on FLAIR (OR 0.53, 95% CI 0.31-0.89; p = 0.021). The combined RFs achieved an area under the curve of 0.814 (95% CI, 0.732-0.896) for grading differentiation. Texture features and metrics extracted from T2, T2GRE, and ADC sequences did not discriminate meningioma grading.
conclusionsHistogram-based first-order RFs from T1, FLAIR, and T1CE may predict meningioma grades preoperatively. Larger, multicenter studies are needed to confirm these findings, providing insights for clinical decision-making and personalized treatment.
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